Dataset.csv

id,left_title,right_title,left_authors,right_authors,left_keywords,right_keywords,label
1,transparency and explainability of ai systems: ethical guidelines in practice,"artificial intelligence, intersectionality, and the future of public health","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","bauer gr,lizotte dj","ai systems,ethical guidelines,explainability,quality requirements,transparency","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
2,transparency and explainability of ai systems: ethical guidelines in practice,fairness & friends in the data science era,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","catania b,guerrini g,accinelli c","ai systems,ethical guidelines,explainability,quality requirements,transparency","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
3,transparency and explainability of ai systems: ethical guidelines in practice,a sociotechnical view of algorithmic fairness,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","dolata m,feuerriegel s,schwabe g","ai systems,ethical guidelines,explainability,quality requirements,transparency","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
4,transparency and explainability of ai systems: ethical guidelines in practice,ai-enabled radio resource allocation in 5g for urllc and embb users,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","elsayed m,erol-kantarci m","ai systems,ethical guidelines,explainability,quality requirements,transparency","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
5,transparency and explainability of ai systems: ethical guidelines in practice,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","giovanola b,tiribelli s","ai systems,ethical guidelines,explainability,quality requirements,transparency","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
6,transparency and explainability of ai systems: ethical guidelines in practice,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","helberger n,araujo t,de vreese ch","ai systems,ethical guidelines,explainability,quality requirements,transparency","algorithmic fairness,automated-decision making,public perceptions",0
7,transparency and explainability of ai systems: ethical guidelines in practice,principal fairness for human and algorithmic decision-making,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","imai k,jiang z","ai systems,ethical guidelines,explainability,quality requirements,transparency","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
8,transparency and explainability of ai systems: ethical guidelines in practice,"introduction to the special issue on ai fairness, trust, and ethics","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","jr lp,bansal g,melville n,...","ai systems,ethical guidelines,explainability,quality requirements,transparency","ai bias,ai fairness,ai trust,artificial intelligence",0
9,transparency and explainability of ai systems: ethical guidelines in practice,exploring bias and fairness in artificial intelligence and machine learning algorithms,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","khakurel ub,abdelmoumin g,bajracharya a,rawat db","ai systems,ethical guidelines,explainability,quality requirements,transparency","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
10,transparency and explainability of ai systems: ethical guidelines in practice,ai & intellectual property: towards an articulated public domain,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s",kop m,"ai systems,ethical guidelines,explainability,quality requirements,transparency","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
11,transparency and explainability of ai systems: ethical guidelines in practice,a survey on datasets for fairness-aware machine learning,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","ai systems,ethical guidelines,explainability,quality requirements,transparency","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
12,transparency and explainability of ai systems: ethical guidelines in practice,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","li c,xing w","ai systems,ethical guidelines,explainability,quality requirements,transparency","fair ai,human-centered computing,math learning,perceived fairness",0
13,transparency and explainability of ai systems: ethical guidelines in practice,trustworthy ai: from principles to practices,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","ai systems,ethical guidelines,explainability,quality requirements,transparency","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
14,transparency and explainability of ai systems: ethical guidelines in practice,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","ai systems,ethical guidelines,explainability,quality requirements,transparency","ai,fairness,machine learning,software development practices",0
15,transparency and explainability of ai systems: ethical guidelines in practice,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","ai systems,ethical guidelines,explainability,quality requirements,transparency","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
16,transparency and explainability of ai systems: ethical guidelines in practice,artificial intelligence in human resources management: challenges and a path forward,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","tambe p,cappelli p,yakubovich v","ai systems,ethical guidelines,explainability,quality requirements,transparency","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
17,transparency and explainability of ai systems: ethical guidelines in practice,artificial intelligence and the public sector—applications and challenges,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","wirtz bw,weyerer jc,geyer c","ai systems,ethical guidelines,explainability,quality requirements,transparency","ai applications,ai challenges,artificial intelligence,public sector",1
18,transparency and explainability of ai systems: ethical guidelines in practice,effective exposure amortizing for fair top-k recommendation,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","yang t,xu z,ai q","ai systems,ethical guidelines,explainability,quality requirements,transparency","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
19,transparency and explainability of ai systems: ethical guidelines in practice,flexible and adaptive fairness-aware learning in non-stationary data streams,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","zhang w,zhang m,zhang j,liu z,...","ai systems,ethical guidelines,explainability,quality requirements,transparency","ai fairness,flexible fairness,online classification",1
20,transparency and explainability of ai systems: ethical guidelines in practice,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","zhou n,zhang z,nair vn,singhal h,chen j","ai systems,ethical guidelines,explainability,quality requirements,transparency","ai,ml,algorithm,bias,fairness",0
21,a perspective on fairness in artificial intelligence,transparency and explainability of ai systems: ethical guidelines in practice,bhattacharya p,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","ai systems,ethical guidelines,explainability,quality requirements,transparency",1
22,a perspective on fairness in artificial intelligence,"artificial intelligence, intersectionality, and the future of public health",bhattacharya p,"bauer gr,lizotte dj","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
23,a perspective on fairness in artificial intelligence,using edge cases to disentangle fairness and solidarity in ai ethics,bhattacharya p,brusseau j,"a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","artificial intelligence,ethics,fairness,philosophy,solidarity",1
24,a perspective on fairness in artificial intelligence,fairness & friends in the data science era,bhattacharya p,"catania b,guerrini g,accinelli c","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",1
25,a perspective on fairness in artificial intelligence,a sociotechnical view of algorithmic fairness,bhattacharya p,"dolata m,feuerriegel s,schwabe g","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
26,a perspective on fairness in artificial intelligence,ai-enabled radio resource allocation in 5g for urllc and embb users,bhattacharya p,"elsayed m,erol-kantarci m","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
27,a perspective on fairness in artificial intelligence,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",bhattacharya p,"fletcher rr,nakeshimana a,...","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",1
28,a perspective on fairness in artificial intelligence,rawls's original position and algorithmic fairness,bhattacharya p,franke u,"a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","algorithmic fairness,original position,veil of ignorance,veil of uncertainty",1
29,a perspective on fairness in artificial intelligence,an ontology for fairness metrics,bhattacharya p,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","bias,fairness metric,machine learning evaluation,rdf knowledge graph",0
30,a perspective on fairness in artificial intelligence,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,bhattacharya p,"gemalmaz ma,yin m","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention",1
31,a perspective on fairness in artificial intelligence,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,bhattacharya p,"giovanola b,tiribelli s","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
32,a perspective on fairness in artificial intelligence,marrying fairness and explainability in supervised learning,bhattacharya p,"grabowicz pa,perello n,mishra a","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","algorithmic fairness,discrimination,explainability,machine learning,supervised learning",1
33,a perspective on fairness in artificial intelligence,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,bhattacharya p,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",1
34,a perspective on fairness in artificial intelligence,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,bhattacharya p,"helberger n,araujo t,de vreese ch","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","algorithmic fairness,automated-decision making,public perceptions",0
35,a perspective on fairness in artificial intelligence,principal fairness for human and algorithmic decision-making,bhattacharya p,"imai k,jiang z","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
36,a perspective on fairness in artificial intelligence,"introduction to the special issue on ai fairness, trust, and ethics",bhattacharya p,"jr lp,bansal g,melville n,...","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","ai bias,ai fairness,ai trust,artificial intelligence",1
37,a perspective on fairness in artificial intelligence,exploring bias and fairness in artificial intelligence and machine learning algorithms,bhattacharya p,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
38,a perspective on fairness in artificial intelligence,ai & intellectual property: towards an articulated public domain,bhattacharya p,kop m,"a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
39,a perspective on fairness in artificial intelligence,a survey on datasets for fairness-aware machine learning,bhattacharya p,"le quy t,roy a,iosifidis v,zhang w,ntoutsi e","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
40,a perspective on fairness in artificial intelligence,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,bhattacharya p,"li c,xing w","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","fair ai,human-centered computing,math learning,perceived fairness",0
41,a perspective on fairness in artificial intelligence,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,bhattacharya p,"li j,chignell textperiodcenteredm","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
42,a perspective on fairness in artificial intelligence,trustworthy ai: from principles to practices,bhattacharya p,"libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
43,a perspective on fairness in artificial intelligence,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",bhattacharya p,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","ai,fairness,machine learning,software development practices",1
44,a perspective on fairness in artificial intelligence,assessing ai fairness in finance,bhattacharya p,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","artificial intelligence,bank data processing",0
45,a perspective on fairness in artificial intelligence,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox",bhattacharya p,"pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
46,a perspective on fairness in artificial intelligence,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,bhattacharya p,"pessach d,shmueli e","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",1
47,a perspective on fairness in artificial intelligence,artificial intelligence evolution: on the virtue of killing in the artificial age,bhattacharya p,puaschunder jm,"a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,",0
48,a perspective on fairness in artificial intelligence,"on the relationship between explanations, fairness perceptions, and decisions",bhattacharya p,"schoeffer j,de-arteaga m,kuehl n","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance",1
49,a perspective on fairness in artificial intelligence,linear versus nonlinear allocation rules in risk sharing under financial fairness,bhattacharya p,schumacher jm,"a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
50,a perspective on fairness in artificial intelligence,artificial intelligence in human resources management: challenges and a path forward,bhattacharya p,"tambe p,cappelli p,yakubovich v","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",1
51,a perspective on fairness in artificial intelligence,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,bhattacharya p,"timan t,grommé f","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","ai,fairness,framework,francisca grommé,ssrn,tjerk timan",0
52,a perspective on fairness in artificial intelligence,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,bhattacharya p,"undefined,ta l,cm c","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
53,a perspective on fairness in artificial intelligence,ai ethics in industry: a research framework,bhattacharya p,"ville v,kai-kristian k,pekka a","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",1
54,a perspective on fairness in artificial intelligence,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,bhattacharya p,"wachter s,mittelstadt b,russell c","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
55,a perspective on fairness in artificial intelligence,user association and resource allocation in full-duplex relay aided noma systems,bhattacharya p,"wang l,ai y,liu n,fei a","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
56,a perspective on fairness in artificial intelligence,artificial intelligence and the public sector—applications and challenges,bhattacharya p,"wirtz bw,weyerer jc,geyer c","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","ai applications,ai challenges,artificial intelligence,public sector",1
57,a perspective on fairness in artificial intelligence,the rising tide of artificial intelligence and business automation: developing an ethical framework,bhattacharya p,"wright sa,schultz ae","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",1
58,a perspective on fairness in artificial intelligence,achieving causal fairness through generative adversarial networks,bhattacharya p,"xu d,wu y,yuan s,zhang l,wu x","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
59,a perspective on fairness in artificial intelligence,algorithmic fairness in computational medicine,bhattacharya p,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","algorithmic fairness,computational medicine",0
60,a perspective on fairness in artificial intelligence,effective exposure amortizing for fair top-k recommendation,bhattacharya p,"yang t,xu z,ai q","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
61,a perspective on fairness in artificial intelligence,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",bhattacharya p,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
62,a perspective on fairness in artificial intelligence,flexible and adaptive fairness-aware learning in non-stationary data streams,bhattacharya p,"zhang w,zhang m,zhang j,liu z,...","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","ai fairness,flexible fairness,online classification",0
63,a perspective on fairness in artificial intelligence,introduction to ai fairness,bhattacharya p,"zhang y,bellamy r,liao qv,singh m","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","algorithmic fairness,bias,decision support,discrimination-aware machine",0
64,a perspective on fairness in artificial intelligence,"bias, fairness and accountability with artificial intelligence and machine learning algorithms",bhattacharya p,"zhou n,zhang z,nair vn,singhal h,chen j","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","ai,ml,algorithm,bias,fairness",1
65,a perspective on fairness in artificial intelligence,applying differential privacy mechanism in artificial intelligence,bhattacharya p,"zhu t,yu ps","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",1
66,using edge cases to disentangle fairness and solidarity in ai ethics,transparency and explainability of ai systems: ethical guidelines in practice,brusseau j,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","artificial intelligence,ethics,fairness,philosophy,solidarity","ai systems,ethical guidelines,explainability,quality requirements,transparency",1
67,using edge cases to disentangle fairness and solidarity in ai ethics,"artificial intelligence, intersectionality, and the future of public health",brusseau j,"bauer gr,lizotte dj","artificial intelligence,ethics,fairness,philosophy,solidarity","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",1
68,using edge cases to disentangle fairness and solidarity in ai ethics,fairness & friends in the data science era,brusseau j,"catania b,guerrini g,accinelli c","artificial intelligence,ethics,fairness,philosophy,solidarity","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",1
69,using edge cases to disentangle fairness and solidarity in ai ethics,a sociotechnical view of algorithmic fairness,brusseau j,"dolata m,feuerriegel s,schwabe g","artificial intelligence,ethics,fairness,philosophy,solidarity","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
70,using edge cases to disentangle fairness and solidarity in ai ethics,ai-enabled radio resource allocation in 5g for urllc and embb users,brusseau j,"elsayed m,erol-kantarci m","artificial intelligence,ethics,fairness,philosophy,solidarity","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
71,using edge cases to disentangle fairness and solidarity in ai ethics,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",brusseau j,"fletcher rr,nakeshimana a,...","artificial intelligence,ethics,fairness,philosophy,solidarity","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",1
72,using edge cases to disentangle fairness and solidarity in ai ethics,rawls's original position and algorithmic fairness,brusseau j,franke u,"artificial intelligence,ethics,fairness,philosophy,solidarity","algorithmic fairness,original position,veil of ignorance,veil of uncertainty",0
73,using edge cases to disentangle fairness and solidarity in ai ethics,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,brusseau j,"giovanola b,tiribelli s","artificial intelligence,ethics,fairness,philosophy,solidarity","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
74,using edge cases to disentangle fairness and solidarity in ai ethics,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,brusseau j,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","artificial intelligence,ethics,fairness,philosophy,solidarity","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",0
75,using edge cases to disentangle fairness and solidarity in ai ethics,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,brusseau j,"helberger n,araujo t,de vreese ch","artificial intelligence,ethics,fairness,philosophy,solidarity","algorithmic fairness,automated-decision making,public perceptions",0
76,using edge cases to disentangle fairness and solidarity in ai ethics,principal fairness for human and algorithmic decision-making,brusseau j,"imai k,jiang z","artificial intelligence,ethics,fairness,philosophy,solidarity","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
77,using edge cases to disentangle fairness and solidarity in ai ethics,"introduction to the special issue on ai fairness, trust, and ethics",brusseau j,"jr lp,bansal g,melville n,...","artificial intelligence,ethics,fairness,philosophy,solidarity","ai bias,ai fairness,ai trust,artificial intelligence",1
78,using edge cases to disentangle fairness and solidarity in ai ethics,exploring bias and fairness in artificial intelligence and machine learning algorithms,brusseau j,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","artificial intelligence,ethics,fairness,philosophy,solidarity","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
79,using edge cases to disentangle fairness and solidarity in ai ethics,ai & intellectual property: towards an articulated public domain,brusseau j,kop m,"artificial intelligence,ethics,fairness,philosophy,solidarity","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
80,using edge cases to disentangle fairness and solidarity in ai ethics,a survey on datasets for fairness-aware machine learning,brusseau j,"le quy t,roy a,iosifidis v,zhang w,ntoutsi e","artificial intelligence,ethics,fairness,philosophy,solidarity","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
81,using edge cases to disentangle fairness and solidarity in ai ethics,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,brusseau j,"li c,xing w","artificial intelligence,ethics,fairness,philosophy,solidarity","fair ai,human-centered computing,math learning,perceived fairness",0
82,using edge cases to disentangle fairness and solidarity in ai ethics,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,brusseau j,"li j,chignell textperiodcenteredm","artificial intelligence,ethics,fairness,philosophy,solidarity","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",1
83,using edge cases to disentangle fairness and solidarity in ai ethics,trustworthy ai: from principles to practices,brusseau j,"libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","artificial intelligence,ethics,fairness,philosophy,solidarity","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
84,using edge cases to disentangle fairness and solidarity in ai ethics,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",brusseau j,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","artificial intelligence,ethics,fairness,philosophy,solidarity","ai,fairness,machine learning,software development practices",0
85,using edge cases to disentangle fairness and solidarity in ai ethics,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox",brusseau j,"pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","artificial intelligence,ethics,fairness,philosophy,solidarity","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
86,using edge cases to disentangle fairness and solidarity in ai ethics,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,brusseau j,"pessach d,shmueli e","artificial intelligence,ethics,fairness,philosophy,solidarity","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",0
87,using edge cases to disentangle fairness and solidarity in ai ethics,linear versus nonlinear allocation rules in risk sharing under financial fairness,brusseau j,schumacher jm,"artificial intelligence,ethics,fairness,philosophy,solidarity","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
88,using edge cases to disentangle fairness and solidarity in ai ethics,artificial intelligence in human resources management: challenges and a path forward,brusseau j,"tambe p,cappelli p,yakubovich v","artificial intelligence,ethics,fairness,philosophy,solidarity","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
89,using edge cases to disentangle fairness and solidarity in ai ethics,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,brusseau j,"timan t,grommé f","artificial intelligence,ethics,fairness,philosophy,solidarity","ai,fairness,framework,francisca grommé,ssrn,tjerk timan",0
90,using edge cases to disentangle fairness and solidarity in ai ethics,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,brusseau j,"undefined,ta l,cm c","artificial intelligence,ethics,fairness,philosophy,solidarity","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
91,using edge cases to disentangle fairness and solidarity in ai ethics,ai ethics in industry: a research framework,brusseau j,"ville v,kai-kristian k,pekka a","artificial intelligence,ethics,fairness,philosophy,solidarity","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",1
92,using edge cases to disentangle fairness and solidarity in ai ethics,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,brusseau j,"wachter s,mittelstadt b,russell c","artificial intelligence,ethics,fairness,philosophy,solidarity","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",1
93,using edge cases to disentangle fairness and solidarity in ai ethics,user association and resource allocation in full-duplex relay aided noma systems,brusseau j,"wang l,ai y,liu n,fei a","artificial intelligence,ethics,fairness,philosophy,solidarity","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
94,using edge cases to disentangle fairness and solidarity in ai ethics,artificial intelligence and the public sector—applications and challenges,brusseau j,"wirtz bw,weyerer jc,geyer c","artificial intelligence,ethics,fairness,philosophy,solidarity","ai applications,ai challenges,artificial intelligence,public sector",1
95,using edge cases to disentangle fairness and solidarity in ai ethics,the rising tide of artificial intelligence and business automation: developing an ethical framework,brusseau j,"wright sa,schultz ae","artificial intelligence,ethics,fairness,philosophy,solidarity","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",1
96,using edge cases to disentangle fairness and solidarity in ai ethics,achieving causal fairness through generative adversarial networks,brusseau j,"xu d,wu y,yuan s,zhang l,wu x","artificial intelligence,ethics,fairness,philosophy,solidarity","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",1
97,using edge cases to disentangle fairness and solidarity in ai ethics,algorithmic fairness in computational medicine,brusseau j,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","artificial intelligence,ethics,fairness,philosophy,solidarity","algorithmic fairness,computational medicine",1
98,using edge cases to disentangle fairness and solidarity in ai ethics,effective exposure amortizing for fair top-k recommendation,brusseau j,"yang t,xu z,ai q","artificial intelligence,ethics,fairness,philosophy,solidarity","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
99,using edge cases to disentangle fairness and solidarity in ai ethics,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",brusseau j,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","artificial intelligence,ethics,fairness,philosophy,solidarity","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
100,using edge cases to disentangle fairness and solidarity in ai ethics,flexible and adaptive fairness-aware learning in non-stationary data streams,brusseau j,"zhang w,zhang m,zhang j,liu z,...","artificial intelligence,ethics,fairness,philosophy,solidarity","ai fairness,flexible fairness,online classification",0
101,using edge cases to disentangle fairness and solidarity in ai ethics,introduction to ai fairness,brusseau j,"zhang y,bellamy r,liao qv,singh m","artificial intelligence,ethics,fairness,philosophy,solidarity","algorithmic fairness,bias,decision support,discrimination-aware machine",0
102,using edge cases to disentangle fairness and solidarity in ai ethics,"bias, fairness and accountability with artificial intelligence and machine learning algorithms",brusseau j,"zhou n,zhang z,nair vn,singhal h,chen j","artificial intelligence,ethics,fairness,philosophy,solidarity","ai,ml,algorithm,bias,fairness",0
103,using edge cases to disentangle fairness and solidarity in ai ethics,applying differential privacy mechanism in artificial intelligence,brusseau j,"zhu t,yu ps","artificial intelligence,ethics,fairness,philosophy,solidarity","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
104,fairness & friends in the data science era,"artificial intelligence, intersectionality, and the future of public health","catania b,guerrini g,accinelli c","bauer gr,lizotte dj","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
105,fairness & friends in the data science era,a sociotechnical view of algorithmic fairness,"catania b,guerrini g,accinelli c","dolata m,feuerriegel s,schwabe g","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
106,fairness & friends in the data science era,ai-enabled radio resource allocation in 5g for urllc and embb users,"catania b,guerrini g,accinelli c","elsayed m,erol-kantarci m","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
107,fairness & friends in the data science era,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"catania b,guerrini g,accinelli c","giovanola b,tiribelli s","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
108,fairness & friends in the data science era,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"catania b,guerrini g,accinelli c","helberger n,araujo t,de vreese ch","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","algorithmic fairness,automated-decision making,public perceptions",0
109,fairness & friends in the data science era,principal fairness for human and algorithmic decision-making,"catania b,guerrini g,accinelli c","imai k,jiang z","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
110,fairness & friends in the data science era,"introduction to the special issue on ai fairness, trust, and ethics","catania b,guerrini g,accinelli c","jr lp,bansal g,melville n,...","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","ai bias,ai fairness,ai trust,artificial intelligence",1
111,fairness & friends in the data science era,exploring bias and fairness in artificial intelligence and machine learning algorithms,"catania b,guerrini g,accinelli c","khakurel ub,abdelmoumin g,bajracharya a,rawat db","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
112,fairness & friends in the data science era,ai & intellectual property: towards an articulated public domain,"catania b,guerrini g,accinelli c",kop m,"data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
113,fairness & friends in the data science era,a survey on datasets for fairness-aware machine learning,"catania b,guerrini g,accinelli c","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
114,fairness & friends in the data science era,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"catania b,guerrini g,accinelli c","li c,xing w","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","fair ai,human-centered computing,math learning,perceived fairness",0
115,fairness & friends in the data science era,trustworthy ai: from principles to practices,"catania b,guerrini g,accinelli c","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
116,fairness & friends in the data science era,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","catania b,guerrini g,accinelli c","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","ai,fairness,machine learning,software development practices",1
117,fairness & friends in the data science era,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","catania b,guerrini g,accinelli c","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
118,fairness & friends in the data science era,artificial intelligence in human resources management: challenges and a path forward,"catania b,guerrini g,accinelli c","tambe p,cappelli p,yakubovich v","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",1
119,fairness & friends in the data science era,artificial intelligence and the public sector—applications and challenges,"catania b,guerrini g,accinelli c","wirtz bw,weyerer jc,geyer c","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","ai applications,ai challenges,artificial intelligence,public sector",0
120,fairness & friends in the data science era,effective exposure amortizing for fair top-k recommendation,"catania b,guerrini g,accinelli c","yang t,xu z,ai q","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
121,fairness & friends in the data science era,flexible and adaptive fairness-aware learning in non-stationary data streams,"catania b,guerrini g,accinelli c","zhang w,zhang m,zhang j,liu z,...","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","ai fairness,flexible fairness,online classification",1
122,fairness & friends in the data science era,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","catania b,guerrini g,accinelli c","zhou n,zhang z,nair vn,singhal h,chen j","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline","ai,ml,algorithm,bias,fairness",1
123,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,transparency and explainability of ai systems: ethical guidelines in practice,"chen j,storchan v,kurshan e","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","algorithmic bias,artificial intelligence,ethics,fairness","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
124,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,"artificial intelligence, intersectionality, and the future of public health","chen j,storchan v,kurshan e","bauer gr,lizotte dj","algorithmic bias,artificial intelligence,ethics,fairness","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
125,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,a perspective on fairness in artificial intelligence,"chen j,storchan v,kurshan e",bhattacharya p,"algorithmic bias,artificial intelligence,ethics,fairness","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws",0
126,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,using edge cases to disentangle fairness and solidarity in ai ethics,"chen j,storchan v,kurshan e",brusseau j,"algorithmic bias,artificial intelligence,ethics,fairness","artificial intelligence,ethics,fairness,philosophy,solidarity",1
127,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,fairness & friends in the data science era,"chen j,storchan v,kurshan e","catania b,guerrini g,accinelli c","algorithmic bias,artificial intelligence,ethics,fairness","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",1
128,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,bias and fairness in machine learning and artificial intelligence,"chen j,storchan v,kurshan e","cirillo d,rementeria mj","algorithmic bias,artificial intelligence,ethics,fairness","artificial intelligence,bias,fairness,model development",0
129,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,a sociotechnical view of algorithmic fairness,"chen j,storchan v,kurshan e","dolata m,feuerriegel s,schwabe g","algorithmic bias,artificial intelligence,ethics,fairness","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
130,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,ai-enabled radio resource allocation in 5g for urllc and embb users,"chen j,storchan v,kurshan e","elsayed m,erol-kantarci m","algorithmic bias,artificial intelligence,ethics,fairness","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
131,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","chen j,storchan v,kurshan e","fletcher rr,nakeshimana a,...","algorithmic bias,artificial intelligence,ethics,fairness","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
132,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,rawls's original position and algorithmic fairness,"chen j,storchan v,kurshan e",franke u,"algorithmic bias,artificial intelligence,ethics,fairness","algorithmic fairness,original position,veil of ignorance,veil of uncertainty",0
133,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,an ontology for fairness metrics,"chen j,storchan v,kurshan e","franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","algorithmic bias,artificial intelligence,ethics,fairness","bias,fairness metric,machine learning evaluation,rdf knowledge graph",0
134,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"chen j,storchan v,kurshan e","gemalmaz ma,yin m","algorithmic bias,artificial intelligence,ethics,fairness","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention",1
135,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"chen j,storchan v,kurshan e","giovanola b,tiribelli s","algorithmic bias,artificial intelligence,ethics,fairness","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",1
136,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,marrying fairness and explainability in supervised learning,"chen j,storchan v,kurshan e","grabowicz pa,perello n,mishra a","algorithmic bias,artificial intelligence,ethics,fairness","algorithmic fairness,discrimination,explainability,machine learning,supervised learning",1
137,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"chen j,storchan v,kurshan e","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","algorithmic bias,artificial intelligence,ethics,fairness","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",1
138,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"chen j,storchan v,kurshan e","helberger n,araujo t,de vreese ch","algorithmic bias,artificial intelligence,ethics,fairness","algorithmic fairness,automated-decision making,public perceptions",0
139,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,principal fairness for human and algorithmic decision-making,"chen j,storchan v,kurshan e","imai k,jiang z","algorithmic bias,artificial intelligence,ethics,fairness","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
140,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,how different groups prioritize ethical values for responsible ai,"chen j,storchan v,kurshan e","jakesch m,buçinca z,amershi s,olteanu a","algorithmic bias,artificial intelligence,ethics,fairness","responsible ai,empirical ethics,value-sensitive design",0
141,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,"introduction to the special issue on ai fairness, trust, and ethics","chen j,storchan v,kurshan e","jr lp,bansal g,melville n,...","algorithmic bias,artificial intelligence,ethics,fairness","ai bias,ai fairness,ai trust,artificial intelligence",1
142,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,exploring bias and fairness in artificial intelligence and machine learning algorithms,"chen j,storchan v,kurshan e","khakurel ub,abdelmoumin g,bajracharya a,rawat db","algorithmic bias,artificial intelligence,ethics,fairness","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
143,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,ai & intellectual property: towards an articulated public domain,"chen j,storchan v,kurshan e",kop m,"algorithmic bias,artificial intelligence,ethics,fairness","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
144,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,a survey on datasets for fairness-aware machine learning,"chen j,storchan v,kurshan e","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","algorithmic bias,artificial intelligence,ethics,fairness","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
145,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"chen j,storchan v,kurshan e","li c,xing w","algorithmic bias,artificial intelligence,ethics,fairness","fair ai,human-centered computing,math learning,perceived fairness",1
146,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"chen j,storchan v,kurshan e","li j,chignell textperiodcenteredm","algorithmic bias,artificial intelligence,ethics,fairness","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
147,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,trustworthy ai: from principles to practices,"chen j,storchan v,kurshan e","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","algorithmic bias,artificial intelligence,ethics,fairness","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
148,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","chen j,storchan v,kurshan e","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","algorithmic bias,artificial intelligence,ethics,fairness","ai,fairness,machine learning,software development practices",1
149,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,assessing ai fairness in finance,"chen j,storchan v,kurshan e","mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","algorithmic bias,artificial intelligence,ethics,fairness","artificial intelligence,bank data processing",1
150,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","chen j,storchan v,kurshan e","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","algorithmic bias,artificial intelligence,ethics,fairness","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
151,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"chen j,storchan v,kurshan e","pessach d,shmueli e","algorithmic bias,artificial intelligence,ethics,fairness","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",0
152,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,artificial intelligence evolution: on the virtue of killing in the artificial age,"chen j,storchan v,kurshan e",puaschunder jm,"algorithmic bias,artificial intelligence,ethics,fairness","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,",0
153,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,how self-perceived reputation affects fairness towards humans and artificial intelligence,"chen j,storchan v,kurshan e","russo pa,duradoni m,guazzini a","algorithmic bias,artificial intelligence,ethics,fairness","artificial intelligence,fairness,reputation,ultimatum game",0
154,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,"on the relationship between explanations, fairness perceptions, and decisions","chen j,storchan v,kurshan e","schoeffer j,de-arteaga m,kuehl n","algorithmic bias,artificial intelligence,ethics,fairness","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance",0
155,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,linear versus nonlinear allocation rules in risk sharing under financial fairness,"chen j,storchan v,kurshan e",schumacher jm,"algorithmic bias,artificial intelligence,ethics,fairness","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
156,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,artificial intelligence in human resources management: challenges and a path forward,"chen j,storchan v,kurshan e","tambe p,cappelli p,yakubovich v","algorithmic bias,artificial intelligence,ethics,fairness","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
157,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"chen j,storchan v,kurshan e","timan t,grommé f","algorithmic bias,artificial intelligence,ethics,fairness","ai,fairness,framework,francisca grommé,ssrn,tjerk timan",0
158,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"chen j,storchan v,kurshan e","undefined,ta l,cm c","algorithmic bias,artificial intelligence,ethics,fairness","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
159,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,ai ethics in industry: a research framework,"chen j,storchan v,kurshan e","ville v,kai-kristian k,pekka a","algorithmic bias,artificial intelligence,ethics,fairness","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",1
160,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"chen j,storchan v,kurshan e","wachter s,mittelstadt b,russell c","algorithmic bias,artificial intelligence,ethics,fairness","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
161,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,user association and resource allocation in full-duplex relay aided noma systems,"chen j,storchan v,kurshan e","wang l,ai y,liu n,fei a","algorithmic bias,artificial intelligence,ethics,fairness","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
162,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,artificial intelligence and the public sector—applications and challenges,"chen j,storchan v,kurshan e","wirtz bw,weyerer jc,geyer c","algorithmic bias,artificial intelligence,ethics,fairness","ai applications,ai challenges,artificial intelligence,public sector",1
163,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,the rising tide of artificial intelligence and business automation: developing an ethical framework,"chen j,storchan v,kurshan e","wright sa,schultz ae","algorithmic bias,artificial intelligence,ethics,fairness","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",1
164,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,achieving causal fairness through generative adversarial networks,"chen j,storchan v,kurshan e","xu d,wu y,yuan s,zhang l,wu x","algorithmic bias,artificial intelligence,ethics,fairness","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",1
165,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,algorithmic fairness in computational medicine,"chen j,storchan v,kurshan e","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","algorithmic bias,artificial intelligence,ethics,fairness","algorithmic fairness,computational medicine",1
166,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,effective exposure amortizing for fair top-k recommendation,"chen j,storchan v,kurshan e","yang t,xu z,ai q","algorithmic bias,artificial intelligence,ethics,fairness","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
167,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","chen j,storchan v,kurshan e","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","algorithmic bias,artificial intelligence,ethics,fairness","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
168,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,flexible and adaptive fairness-aware learning in non-stationary data streams,"chen j,storchan v,kurshan e","zhang w,zhang m,zhang j,liu z,...","algorithmic bias,artificial intelligence,ethics,fairness","ai fairness,flexible fairness,online classification",0
169,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,introduction to ai fairness,"chen j,storchan v,kurshan e","zhang y,bellamy r,liao qv,singh m","algorithmic bias,artificial intelligence,ethics,fairness","algorithmic fairness,bias,decision support,discrimination-aware machine",0
170,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","chen j,storchan v,kurshan e","zhou n,zhang z,nair vn,singhal h,chen j","algorithmic bias,artificial intelligence,ethics,fairness","ai,ml,algorithm,bias,fairness",1
171,beyond fairness metrics: roadblocks and challenges for ethical ai in practice,applying differential privacy mechanism in artificial intelligence,"chen j,storchan v,kurshan e","zhu t,yu ps","algorithmic bias,artificial intelligence,ethics,fairness","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
172,bias and fairness in machine learning and artificial intelligence,transparency and explainability of ai systems: ethical guidelines in practice,"cirillo d,rementeria mj","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","artificial intelligence,bias,fairness,model development","ai systems,ethical guidelines,explainability,quality requirements,transparency",1
173,bias and fairness in machine learning and artificial intelligence,"artificial intelligence, intersectionality, and the future of public health","cirillo d,rementeria mj","bauer gr,lizotte dj","artificial intelligence,bias,fairness,model development","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",1
174,bias and fairness in machine learning and artificial intelligence,a perspective on fairness in artificial intelligence,"cirillo d,rementeria mj",bhattacharya p,"artificial intelligence,bias,fairness,model development","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws",1
175,bias and fairness in machine learning and artificial intelligence,using edge cases to disentangle fairness and solidarity in ai ethics,"cirillo d,rementeria mj",brusseau j,"artificial intelligence,bias,fairness,model development","artificial intelligence,ethics,fairness,philosophy,solidarity",0
176,bias and fairness in machine learning and artificial intelligence,fairness & friends in the data science era,"cirillo d,rementeria mj","catania b,guerrini g,accinelli c","artificial intelligence,bias,fairness,model development","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",1
177,bias and fairness in machine learning and artificial intelligence,a sociotechnical view of algorithmic fairness,"cirillo d,rementeria mj","dolata m,feuerriegel s,schwabe g","artificial intelligence,bias,fairness,model development","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
178,bias and fairness in machine learning and artificial intelligence,ai-enabled radio resource allocation in 5g for urllc and embb users,"cirillo d,rementeria mj","elsayed m,erol-kantarci m","artificial intelligence,bias,fairness,model development","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
179,bias and fairness in machine learning and artificial intelligence,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","cirillo d,rementeria mj","fletcher rr,nakeshimana a,...","artificial intelligence,bias,fairness,model development","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",1
180,bias and fairness in machine learning and artificial intelligence,rawls's original position and algorithmic fairness,"cirillo d,rementeria mj",franke u,"artificial intelligence,bias,fairness,model development","algorithmic fairness,original position,veil of ignorance,veil of uncertainty",1
181,bias and fairness in machine learning and artificial intelligence,an ontology for fairness metrics,"cirillo d,rementeria mj","franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","artificial intelligence,bias,fairness,model development","bias,fairness metric,machine learning evaluation,rdf knowledge graph",0
182,bias and fairness in machine learning and artificial intelligence,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"cirillo d,rementeria mj","gemalmaz ma,yin m","artificial intelligence,bias,fairness,model development","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention",1
183,bias and fairness in machine learning and artificial intelligence,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"cirillo d,rementeria mj","giovanola b,tiribelli s","artificial intelligence,bias,fairness,model development","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
184,bias and fairness in machine learning and artificial intelligence,marrying fairness and explainability in supervised learning,"cirillo d,rementeria mj","grabowicz pa,perello n,mishra a","artificial intelligence,bias,fairness,model development","algorithmic fairness,discrimination,explainability,machine learning,supervised learning",1
185,bias and fairness in machine learning and artificial intelligence,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"cirillo d,rementeria mj","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","artificial intelligence,bias,fairness,model development","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",1
186,bias and fairness in machine learning and artificial intelligence,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"cirillo d,rementeria mj","helberger n,araujo t,de vreese ch","artificial intelligence,bias,fairness,model development","algorithmic fairness,automated-decision making,public perceptions",1
187,bias and fairness in machine learning and artificial intelligence,principal fairness for human and algorithmic decision-making,"cirillo d,rementeria mj","imai k,jiang z","artificial intelligence,bias,fairness,model development","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
188,bias and fairness in machine learning and artificial intelligence,how different groups prioritize ethical values for responsible ai,"cirillo d,rementeria mj","jakesch m,buçinca z,amershi s,olteanu a","artificial intelligence,bias,fairness,model development","responsible ai,empirical ethics,value-sensitive design",0
189,bias and fairness in machine learning and artificial intelligence,"introduction to the special issue on ai fairness, trust, and ethics","cirillo d,rementeria mj","jr lp,bansal g,melville n,...","artificial intelligence,bias,fairness,model development","ai bias,ai fairness,ai trust,artificial intelligence",1
190,bias and fairness in machine learning and artificial intelligence,exploring bias and fairness in artificial intelligence and machine learning algorithms,"cirillo d,rementeria mj","khakurel ub,abdelmoumin g,bajracharya a,rawat db","artificial intelligence,bias,fairness,model development","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
191,bias and fairness in machine learning and artificial intelligence,ai & intellectual property: towards an articulated public domain,"cirillo d,rementeria mj",kop m,"artificial intelligence,bias,fairness,model development","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
192,bias and fairness in machine learning and artificial intelligence,a survey on datasets for fairness-aware machine learning,"cirillo d,rementeria mj","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","artificial intelligence,bias,fairness,model development","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
193,bias and fairness in machine learning and artificial intelligence,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"cirillo d,rementeria mj","li c,xing w","artificial intelligence,bias,fairness,model development","fair ai,human-centered computing,math learning,perceived fairness",0
194,bias and fairness in machine learning and artificial intelligence,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"cirillo d,rementeria mj","li j,chignell textperiodcenteredm","artificial intelligence,bias,fairness,model development","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",1
195,bias and fairness in machine learning and artificial intelligence,trustworthy ai: from principles to practices,"cirillo d,rementeria mj","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","artificial intelligence,bias,fairness,model development","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
196,bias and fairness in machine learning and artificial intelligence,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","cirillo d,rementeria mj","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","artificial intelligence,bias,fairness,model development","ai,fairness,machine learning,software development practices",1
197,bias and fairness in machine learning and artificial intelligence,assessing ai fairness in finance,"cirillo d,rementeria mj","mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","artificial intelligence,bias,fairness,model development","artificial intelligence,bank data processing",1
198,bias and fairness in machine learning and artificial intelligence,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","cirillo d,rementeria mj","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","artificial intelligence,bias,fairness,model development","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
199,bias and fairness in machine learning and artificial intelligence,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"cirillo d,rementeria mj","pessach d,shmueli e","artificial intelligence,bias,fairness,model development","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",1
200,bias and fairness in machine learning and artificial intelligence,artificial intelligence evolution: on the virtue of killing in the artificial age,"cirillo d,rementeria mj",puaschunder jm,"artificial intelligence,bias,fairness,model development","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,",1
201,bias and fairness in machine learning and artificial intelligence,"on the relationship between explanations, fairness perceptions, and decisions","cirillo d,rementeria mj","schoeffer j,de-arteaga m,kuehl n","artificial intelligence,bias,fairness,model development","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance",0
202,bias and fairness in machine learning and artificial intelligence,linear versus nonlinear allocation rules in risk sharing under financial fairness,"cirillo d,rementeria mj",schumacher jm,"artificial intelligence,bias,fairness,model development","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
203,bias and fairness in machine learning and artificial intelligence,artificial intelligence in human resources management: challenges and a path forward,"cirillo d,rementeria mj","tambe p,cappelli p,yakubovich v","artificial intelligence,bias,fairness,model development","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",1
204,bias and fairness in machine learning and artificial intelligence,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"cirillo d,rementeria mj","timan t,grommé f","artificial intelligence,bias,fairness,model development","ai,fairness,framework,francisca grommé,ssrn,tjerk timan",0
205,bias and fairness in machine learning and artificial intelligence,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"cirillo d,rementeria mj","undefined,ta l,cm c","artificial intelligence,bias,fairness,model development","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
206,bias and fairness in machine learning and artificial intelligence,ai ethics in industry: a research framework,"cirillo d,rementeria mj","ville v,kai-kristian k,pekka a","artificial intelligence,bias,fairness,model development","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",1
207,bias and fairness in machine learning and artificial intelligence,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"cirillo d,rementeria mj","wachter s,mittelstadt b,russell c","artificial intelligence,bias,fairness,model development","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",1
208,bias and fairness in machine learning and artificial intelligence,user association and resource allocation in full-duplex relay aided noma systems,"cirillo d,rementeria mj","wang l,ai y,liu n,fei a","artificial intelligence,bias,fairness,model development","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
209,bias and fairness in machine learning and artificial intelligence,artificial intelligence and the public sector—applications and challenges,"cirillo d,rementeria mj","wirtz bw,weyerer jc,geyer c","artificial intelligence,bias,fairness,model development","ai applications,ai challenges,artificial intelligence,public sector",1
210,bias and fairness in machine learning and artificial intelligence,the rising tide of artificial intelligence and business automation: developing an ethical framework,"cirillo d,rementeria mj","wright sa,schultz ae","artificial intelligence,bias,fairness,model development","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",1
211,bias and fairness in machine learning and artificial intelligence,achieving causal fairness through generative adversarial networks,"cirillo d,rementeria mj","xu d,wu y,yuan s,zhang l,wu x","artificial intelligence,bias,fairness,model development","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
212,bias and fairness in machine learning and artificial intelligence,algorithmic fairness in computational medicine,"cirillo d,rementeria mj","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","artificial intelligence,bias,fairness,model development","algorithmic fairness,computational medicine",0
213,bias and fairness in machine learning and artificial intelligence,effective exposure amortizing for fair top-k recommendation,"cirillo d,rementeria mj","yang t,xu z,ai q","artificial intelligence,bias,fairness,model development","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
214,bias and fairness in machine learning and artificial intelligence,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","cirillo d,rementeria mj","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","artificial intelligence,bias,fairness,model development","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
215,bias and fairness in machine learning and artificial intelligence,flexible and adaptive fairness-aware learning in non-stationary data streams,"cirillo d,rementeria mj","zhang w,zhang m,zhang j,liu z,...","artificial intelligence,bias,fairness,model development","ai fairness,flexible fairness,online classification",1
216,bias and fairness in machine learning and artificial intelligence,introduction to ai fairness,"cirillo d,rementeria mj","zhang y,bellamy r,liao qv,singh m","artificial intelligence,bias,fairness,model development","algorithmic fairness,bias,decision support,discrimination-aware machine",0
217,bias and fairness in machine learning and artificial intelligence,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","cirillo d,rementeria mj","zhou n,zhang z,nair vn,singhal h,chen j","artificial intelligence,bias,fairness,model development","ai,ml,algorithm,bias,fairness",1
218,bias and fairness in machine learning and artificial intelligence,applying differential privacy mechanism in artificial intelligence,"cirillo d,rementeria mj","zhu t,yu ps","artificial intelligence,bias,fairness,model development","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",1
219,a sociotechnical view of algorithmic fairness,"artificial intelligence, intersectionality, and the future of public health","dolata m,feuerriegel s,schwabe g","bauer gr,lizotte dj","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
220,a sociotechnical view of algorithmic fairness,ai-enabled radio resource allocation in 5g for urllc and embb users,"dolata m,feuerriegel s,schwabe g","elsayed m,erol-kantarci m","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
221,a sociotechnical view of algorithmic fairness,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","dolata m,feuerriegel s,schwabe g","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
222,a sociotechnical view of algorithmic fairness,flexible and adaptive fairness-aware learning in non-stationary data streams,"dolata m,feuerriegel s,schwabe g","zhang w,zhang m,zhang j,liu z,...","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art","ai fairness,flexible fairness,online classification",0
223,ai-enabled radio resource allocation in 5g for urllc and embb users,"artificial intelligence, intersectionality, and the future of public health","elsayed m,erol-kantarci m","bauer gr,lizotte dj","5g mobile communication,delays,quality of service,reliability,resource management,throughput","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
224,ai-enabled radio resource allocation in 5g for urllc and embb users,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","elsayed m,erol-kantarci m","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","5g mobile communication,delays,quality of service,reliability,resource management,throughput","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
225,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",transparency and explainability of ai systems: ethical guidelines in practice,"fletcher rr,nakeshimana a,...","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
226,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","artificial intelligence, intersectionality, and the future of public health","fletcher rr,nakeshimana a,...","bauer gr,lizotte dj","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",1
227,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",fairness & friends in the data science era,"fletcher rr,nakeshimana a,...","catania b,guerrini g,accinelli c","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
228,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",a sociotechnical view of algorithmic fairness,"fletcher rr,nakeshimana a,...","dolata m,feuerriegel s,schwabe g","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
229,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",ai-enabled radio resource allocation in 5g for urllc and embb users,"fletcher rr,nakeshimana a,...","elsayed m,erol-kantarci m","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
230,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"fletcher rr,nakeshimana a,...","giovanola b,tiribelli s","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",1
231,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"fletcher rr,nakeshimana a,...","helberger n,araujo t,de vreese ch","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","algorithmic fairness,automated-decision making,public perceptions",1
232,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",principal fairness for human and algorithmic decision-making,"fletcher rr,nakeshimana a,...","imai k,jiang z","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
233,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","introduction to the special issue on ai fairness, trust, and ethics","fletcher rr,nakeshimana a,...","jr lp,bansal g,melville n,...","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","ai bias,ai fairness,ai trust,artificial intelligence",1
234,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",exploring bias and fairness in artificial intelligence and machine learning algorithms,"fletcher rr,nakeshimana a,...","khakurel ub,abdelmoumin g,bajracharya a,rawat db","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
235,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",ai & intellectual property: towards an articulated public domain,"fletcher rr,nakeshimana a,...",kop m,"appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
236,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",a survey on datasets for fairness-aware machine learning,"fletcher rr,nakeshimana a,...","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
237,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"fletcher rr,nakeshimana a,...","li c,xing w","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","fair ai,human-centered computing,math learning,perceived fairness",1
238,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",trustworthy ai: from principles to practices,"fletcher rr,nakeshimana a,...","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
239,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","fletcher rr,nakeshimana a,...","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","ai,fairness,machine learning,software development practices",1
240,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","fletcher rr,nakeshimana a,...","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
241,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",artificial intelligence in human resources management: challenges and a path forward,"fletcher rr,nakeshimana a,...","tambe p,cappelli p,yakubovich v","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",1
242,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",ai ethics in industry: a research framework,"fletcher rr,nakeshimana a,...","ville v,kai-kristian k,pekka a","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
243,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",artificial intelligence and the public sector—applications and challenges,"fletcher rr,nakeshimana a,...","wirtz bw,weyerer jc,geyer c","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","ai applications,ai challenges,artificial intelligence,public sector",1
244,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",effective exposure amortizing for fair top-k recommendation,"fletcher rr,nakeshimana a,...","yang t,xu z,ai q","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
245,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",flexible and adaptive fairness-aware learning in non-stationary data streams,"fletcher rr,nakeshimana a,...","zhang w,zhang m,zhang j,liu z,...","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","ai fairness,flexible fairness,online classification",1
246,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","bias, fairness and accountability with artificial intelligence and machine learning algorithms","fletcher rr,nakeshimana a,...","zhou n,zhang z,nair vn,singhal h,chen j","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","ai,ml,algorithm,bias,fairness",1
247,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",applying differential privacy mechanism in artificial intelligence,"fletcher rr,nakeshimana a,...","zhu t,yu ps","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",1
248,rawls's original position and algorithmic fairness,transparency and explainability of ai systems: ethical guidelines in practice,franke u,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
249,rawls's original position and algorithmic fairness,"artificial intelligence, intersectionality, and the future of public health",franke u,"bauer gr,lizotte dj","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
250,rawls's original position and algorithmic fairness,fairness & friends in the data science era,franke u,"catania b,guerrini g,accinelli c","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
251,rawls's original position and algorithmic fairness,a sociotechnical view of algorithmic fairness,franke u,"dolata m,feuerriegel s,schwabe g","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
252,rawls's original position and algorithmic fairness,ai-enabled radio resource allocation in 5g for urllc and embb users,franke u,"elsayed m,erol-kantarci m","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
253,rawls's original position and algorithmic fairness,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",franke u,"fletcher rr,nakeshimana a,...","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
254,rawls's original position and algorithmic fairness,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,franke u,"giovanola b,tiribelli s","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
255,rawls's original position and algorithmic fairness,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,franke u,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",0
256,rawls's original position and algorithmic fairness,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,franke u,"helberger n,araujo t,de vreese ch","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","algorithmic fairness,automated-decision making,public perceptions",0
257,rawls's original position and algorithmic fairness,principal fairness for human and algorithmic decision-making,franke u,"imai k,jiang z","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
258,rawls's original position and algorithmic fairness,"introduction to the special issue on ai fairness, trust, and ethics",franke u,"jr lp,bansal g,melville n,...","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","ai bias,ai fairness,ai trust,artificial intelligence",0
259,rawls's original position and algorithmic fairness,exploring bias and fairness in artificial intelligence and machine learning algorithms,franke u,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",0
260,rawls's original position and algorithmic fairness,ai & intellectual property: towards an articulated public domain,franke u,kop m,"algorithmic fairness,original position,veil of ignorance,veil of uncertainty","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
261,rawls's original position and algorithmic fairness,a survey on datasets for fairness-aware machine learning,franke u,"le quy t,roy a,iosifidis v,zhang w,ntoutsi e","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
262,rawls's original position and algorithmic fairness,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,franke u,"li c,xing w","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","fair ai,human-centered computing,math learning,perceived fairness",0
263,rawls's original position and algorithmic fairness,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,franke u,"li j,chignell textperiodcenteredm","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
264,rawls's original position and algorithmic fairness,trustworthy ai: from principles to practices,franke u,"libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
265,rawls's original position and algorithmic fairness,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",franke u,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","ai,fairness,machine learning,software development practices",0
266,rawls's original position and algorithmic fairness,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox",franke u,"pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
267,rawls's original position and algorithmic fairness,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,franke u,"pessach d,shmueli e","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",1
268,rawls's original position and algorithmic fairness,linear versus nonlinear allocation rules in risk sharing under financial fairness,franke u,schumacher jm,"algorithmic fairness,original position,veil of ignorance,veil of uncertainty","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",1
269,rawls's original position and algorithmic fairness,artificial intelligence in human resources management: challenges and a path forward,franke u,"tambe p,cappelli p,yakubovich v","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
270,rawls's original position and algorithmic fairness,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,franke u,"undefined,ta l,cm c","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
271,rawls's original position and algorithmic fairness,ai ethics in industry: a research framework,franke u,"ville v,kai-kristian k,pekka a","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
272,rawls's original position and algorithmic fairness,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,franke u,"wachter s,mittelstadt b,russell c","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
273,rawls's original position and algorithmic fairness,user association and resource allocation in full-duplex relay aided noma systems,franke u,"wang l,ai y,liu n,fei a","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
274,rawls's original position and algorithmic fairness,artificial intelligence and the public sector—applications and challenges,franke u,"wirtz bw,weyerer jc,geyer c","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","ai applications,ai challenges,artificial intelligence,public sector",0
275,rawls's original position and algorithmic fairness,the rising tide of artificial intelligence and business automation: developing an ethical framework,franke u,"wright sa,schultz ae","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",0
276,rawls's original position and algorithmic fairness,achieving causal fairness through generative adversarial networks,franke u,"xu d,wu y,yuan s,zhang l,wu x","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
277,rawls's original position and algorithmic fairness,algorithmic fairness in computational medicine,franke u,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","algorithmic fairness,computational medicine",1
278,rawls's original position and algorithmic fairness,effective exposure amortizing for fair top-k recommendation,franke u,"yang t,xu z,ai q","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
279,rawls's original position and algorithmic fairness,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",franke u,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
280,rawls's original position and algorithmic fairness,flexible and adaptive fairness-aware learning in non-stationary data streams,franke u,"zhang w,zhang m,zhang j,liu z,...","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","ai fairness,flexible fairness,online classification",0
281,rawls's original position and algorithmic fairness,introduction to ai fairness,franke u,"zhang y,bellamy r,liao qv,singh m","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","algorithmic fairness,bias,decision support,discrimination-aware machine",0
282,rawls's original position and algorithmic fairness,"bias, fairness and accountability with artificial intelligence and machine learning algorithms",franke u,"zhou n,zhang z,nair vn,singhal h,chen j","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","ai,ml,algorithm,bias,fairness",0
283,rawls's original position and algorithmic fairness,applying differential privacy mechanism in artificial intelligence,franke u,"zhu t,yu ps","algorithmic fairness,original position,veil of ignorance,veil of uncertainty","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
284,an ontology for fairness metrics,transparency and explainability of ai systems: ethical guidelines in practice,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","bias,fairness metric,machine learning evaluation,rdf knowledge graph","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
285,an ontology for fairness metrics,"artificial intelligence, intersectionality, and the future of public health","franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","bauer gr,lizotte dj","bias,fairness metric,machine learning evaluation,rdf knowledge graph","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
286,an ontology for fairness metrics,using edge cases to disentangle fairness and solidarity in ai ethics,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl",brusseau j,"bias,fairness metric,machine learning evaluation,rdf knowledge graph","artificial intelligence,ethics,fairness,philosophy,solidarity",0
287,an ontology for fairness metrics,fairness & friends in the data science era,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","catania b,guerrini g,accinelli c","bias,fairness metric,machine learning evaluation,rdf knowledge graph","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
288,an ontology for fairness metrics,a sociotechnical view of algorithmic fairness,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","dolata m,feuerriegel s,schwabe g","bias,fairness metric,machine learning evaluation,rdf knowledge graph","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
289,an ontology for fairness metrics,ai-enabled radio resource allocation in 5g for urllc and embb users,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","elsayed m,erol-kantarci m","bias,fairness metric,machine learning evaluation,rdf knowledge graph","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
290,an ontology for fairness metrics,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","fletcher rr,nakeshimana a,...","bias,fairness metric,machine learning evaluation,rdf knowledge graph","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
291,an ontology for fairness metrics,rawls's original position and algorithmic fairness,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl",franke u,"bias,fairness metric,machine learning evaluation,rdf knowledge graph","algorithmic fairness,original position,veil of ignorance,veil of uncertainty",0
292,an ontology for fairness metrics,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","gemalmaz ma,yin m","bias,fairness metric,machine learning evaluation,rdf knowledge graph","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention",0
293,an ontology for fairness metrics,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","giovanola b,tiribelli s","bias,fairness metric,machine learning evaluation,rdf knowledge graph","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
294,an ontology for fairness metrics,marrying fairness and explainability in supervised learning,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","grabowicz pa,perello n,mishra a","bias,fairness metric,machine learning evaluation,rdf knowledge graph","algorithmic fairness,discrimination,explainability,machine learning,supervised learning",0
295,an ontology for fairness metrics,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","bias,fairness metric,machine learning evaluation,rdf knowledge graph","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",0
296,an ontology for fairness metrics,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","helberger n,araujo t,de vreese ch","bias,fairness metric,machine learning evaluation,rdf knowledge graph","algorithmic fairness,automated-decision making,public perceptions",0
297,an ontology for fairness metrics,principal fairness for human and algorithmic decision-making,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","imai k,jiang z","bias,fairness metric,machine learning evaluation,rdf knowledge graph","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
298,an ontology for fairness metrics,"introduction to the special issue on ai fairness, trust, and ethics","franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","jr lp,bansal g,melville n,...","bias,fairness metric,machine learning evaluation,rdf knowledge graph","ai bias,ai fairness,ai trust,artificial intelligence",0
299,an ontology for fairness metrics,exploring bias and fairness in artificial intelligence and machine learning algorithms,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","khakurel ub,abdelmoumin g,bajracharya a,rawat db","bias,fairness metric,machine learning evaluation,rdf knowledge graph","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",0
300,an ontology for fairness metrics,ai & intellectual property: towards an articulated public domain,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl",kop m,"bias,fairness metric,machine learning evaluation,rdf knowledge graph","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
301,an ontology for fairness metrics,a survey on datasets for fairness-aware machine learning,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","bias,fairness metric,machine learning evaluation,rdf knowledge graph","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
302,an ontology for fairness metrics,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","li c,xing w","bias,fairness metric,machine learning evaluation,rdf knowledge graph","fair ai,human-centered computing,math learning,perceived fairness",0
303,an ontology for fairness metrics,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","li j,chignell textperiodcenteredm","bias,fairness metric,machine learning evaluation,rdf knowledge graph","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
304,an ontology for fairness metrics,trustworthy ai: from principles to practices,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","bias,fairness metric,machine learning evaluation,rdf knowledge graph","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",1
305,an ontology for fairness metrics,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","bias,fairness metric,machine learning evaluation,rdf knowledge graph","ai,fairness,machine learning,software development practices",0
306,an ontology for fairness metrics,assessing ai fairness in finance,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","bias,fairness metric,machine learning evaluation,rdf knowledge graph","artificial intelligence,bank data processing",0
307,an ontology for fairness metrics,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","bias,fairness metric,machine learning evaluation,rdf knowledge graph","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
308,an ontology for fairness metrics,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","pessach d,shmueli e","bias,fairness metric,machine learning evaluation,rdf knowledge graph","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",0
309,an ontology for fairness metrics,artificial intelligence evolution: on the virtue of killing in the artificial age,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl",puaschunder jm,"bias,fairness metric,machine learning evaluation,rdf knowledge graph","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,",0
310,an ontology for fairness metrics,"on the relationship between explanations, fairness perceptions, and decisions","franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","schoeffer j,de-arteaga m,kuehl n","bias,fairness metric,machine learning evaluation,rdf knowledge graph","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance",0
311,an ontology for fairness metrics,linear versus nonlinear allocation rules in risk sharing under financial fairness,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl",schumacher jm,"bias,fairness metric,machine learning evaluation,rdf knowledge graph","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
312,an ontology for fairness metrics,artificial intelligence in human resources management: challenges and a path forward,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","tambe p,cappelli p,yakubovich v","bias,fairness metric,machine learning evaluation,rdf knowledge graph","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
313,an ontology for fairness metrics,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","timan t,grommé f","bias,fairness metric,machine learning evaluation,rdf knowledge graph","ai,fairness,framework,francisca grommé,ssrn,tjerk timan",0
314,an ontology for fairness metrics,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","undefined,ta l,cm c","bias,fairness metric,machine learning evaluation,rdf knowledge graph","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
315,an ontology for fairness metrics,ai ethics in industry: a research framework,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","ville v,kai-kristian k,pekka a","bias,fairness metric,machine learning evaluation,rdf knowledge graph","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",1
316,an ontology for fairness metrics,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","wachter s,mittelstadt b,russell c","bias,fairness metric,machine learning evaluation,rdf knowledge graph","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
317,an ontology for fairness metrics,user association and resource allocation in full-duplex relay aided noma systems,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","wang l,ai y,liu n,fei a","bias,fairness metric,machine learning evaluation,rdf knowledge graph","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
318,an ontology for fairness metrics,artificial intelligence and the public sector—applications and challenges,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","wirtz bw,weyerer jc,geyer c","bias,fairness metric,machine learning evaluation,rdf knowledge graph","ai applications,ai challenges,artificial intelligence,public sector",0
319,an ontology for fairness metrics,the rising tide of artificial intelligence and business automation: developing an ethical framework,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","wright sa,schultz ae","bias,fairness metric,machine learning evaluation,rdf knowledge graph","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",0
320,an ontology for fairness metrics,achieving causal fairness through generative adversarial networks,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","xu d,wu y,yuan s,zhang l,wu x","bias,fairness metric,machine learning evaluation,rdf knowledge graph","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",1
321,an ontology for fairness metrics,algorithmic fairness in computational medicine,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","bias,fairness metric,machine learning evaluation,rdf knowledge graph","algorithmic fairness,computational medicine",1
322,an ontology for fairness metrics,effective exposure amortizing for fair top-k recommendation,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","yang t,xu z,ai q","bias,fairness metric,machine learning evaluation,rdf knowledge graph","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
323,an ontology for fairness metrics,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","bias,fairness metric,machine learning evaluation,rdf knowledge graph","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
324,an ontology for fairness metrics,flexible and adaptive fairness-aware learning in non-stationary data streams,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","zhang w,zhang m,zhang j,liu z,...","bias,fairness metric,machine learning evaluation,rdf knowledge graph","ai fairness,flexible fairness,online classification",0
325,an ontology for fairness metrics,introduction to ai fairness,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","zhang y,bellamy r,liao qv,singh m","bias,fairness metric,machine learning evaluation,rdf knowledge graph","algorithmic fairness,bias,decision support,discrimination-aware machine",1
326,an ontology for fairness metrics,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","zhou n,zhang z,nair vn,singhal h,chen j","bias,fairness metric,machine learning evaluation,rdf knowledge graph","ai,ml,algorithm,bias,fairness",0
327,an ontology for fairness metrics,applying differential privacy mechanism in artificial intelligence,"franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","zhu t,yu ps","bias,fairness metric,machine learning evaluation,rdf knowledge graph","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
328,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,transparency and explainability of ai systems: ethical guidelines in practice,"gemalmaz ma,yin m","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","ai systems,ethical guidelines,explainability,quality requirements,transparency",1
329,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"artificial intelligence, intersectionality, and the future of public health","gemalmaz ma,yin m","bauer gr,lizotte dj","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
330,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,using edge cases to disentangle fairness and solidarity in ai ethics,"gemalmaz ma,yin m",brusseau j,"ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","artificial intelligence,ethics,fairness,philosophy,solidarity",0
331,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,fairness & friends in the data science era,"gemalmaz ma,yin m","catania b,guerrini g,accinelli c","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
332,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,a sociotechnical view of algorithmic fairness,"gemalmaz ma,yin m","dolata m,feuerriegel s,schwabe g","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
333,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,ai-enabled radio resource allocation in 5g for urllc and embb users,"gemalmaz ma,yin m","elsayed m,erol-kantarci m","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
334,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","gemalmaz ma,yin m","fletcher rr,nakeshimana a,...","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
335,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,rawls's original position and algorithmic fairness,"gemalmaz ma,yin m",franke u,"ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","algorithmic fairness,original position,veil of ignorance,veil of uncertainty",0
336,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"gemalmaz ma,yin m","giovanola b,tiribelli s","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
337,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,marrying fairness and explainability in supervised learning,"gemalmaz ma,yin m","grabowicz pa,perello n,mishra a","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","algorithmic fairness,discrimination,explainability,machine learning,supervised learning",0
338,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"gemalmaz ma,yin m","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",1
339,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"gemalmaz ma,yin m","helberger n,araujo t,de vreese ch","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","algorithmic fairness,automated-decision making,public perceptions",0
340,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,principal fairness for human and algorithmic decision-making,"gemalmaz ma,yin m","imai k,jiang z","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
341,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"introduction to the special issue on ai fairness, trust, and ethics","gemalmaz ma,yin m","jr lp,bansal g,melville n,...","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","ai bias,ai fairness,ai trust,artificial intelligence",0
342,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,exploring bias and fairness in artificial intelligence and machine learning algorithms,"gemalmaz ma,yin m","khakurel ub,abdelmoumin g,bajracharya a,rawat db","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
343,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,ai & intellectual property: towards an articulated public domain,"gemalmaz ma,yin m",kop m,"ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
344,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,a survey on datasets for fairness-aware machine learning,"gemalmaz ma,yin m","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
345,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"gemalmaz ma,yin m","li c,xing w","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","fair ai,human-centered computing,math learning,perceived fairness",1
346,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"gemalmaz ma,yin m","li j,chignell textperiodcenteredm","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
347,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,trustworthy ai: from principles to practices,"gemalmaz ma,yin m","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
348,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","gemalmaz ma,yin m","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","ai,fairness,machine learning,software development practices",1
349,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","gemalmaz ma,yin m","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
350,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"gemalmaz ma,yin m","pessach d,shmueli e","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",1
351,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"on the relationship between explanations, fairness perceptions, and decisions","gemalmaz ma,yin m","schoeffer j,de-arteaga m,kuehl n","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance",1
352,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,linear versus nonlinear allocation rules in risk sharing under financial fairness,"gemalmaz ma,yin m",schumacher jm,"ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
353,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,artificial intelligence in human resources management: challenges and a path forward,"gemalmaz ma,yin m","tambe p,cappelli p,yakubovich v","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
354,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"gemalmaz ma,yin m","timan t,grommé f","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","ai,fairness,framework,francisca grommé,ssrn,tjerk timan",1
355,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"gemalmaz ma,yin m","undefined,ta l,cm c","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
356,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,ai ethics in industry: a research framework,"gemalmaz ma,yin m","ville v,kai-kristian k,pekka a","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
357,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"gemalmaz ma,yin m","wachter s,mittelstadt b,russell c","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",1
358,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,user association and resource allocation in full-duplex relay aided noma systems,"gemalmaz ma,yin m","wang l,ai y,liu n,fei a","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
359,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,artificial intelligence and the public sector—applications and challenges,"gemalmaz ma,yin m","wirtz bw,weyerer jc,geyer c","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","ai applications,ai challenges,artificial intelligence,public sector",0
360,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,the rising tide of artificial intelligence and business automation: developing an ethical framework,"gemalmaz ma,yin m","wright sa,schultz ae","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",0
361,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,achieving causal fairness through generative adversarial networks,"gemalmaz ma,yin m","xu d,wu y,yuan s,zhang l,wu x","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
362,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,algorithmic fairness in computational medicine,"gemalmaz ma,yin m","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","algorithmic fairness,computational medicine",0
363,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,effective exposure amortizing for fair top-k recommendation,"gemalmaz ma,yin m","yang t,xu z,ai q","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
364,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","gemalmaz ma,yin m","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
365,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,flexible and adaptive fairness-aware learning in non-stationary data streams,"gemalmaz ma,yin m","zhang w,zhang m,zhang j,liu z,...","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","ai fairness,flexible fairness,online classification",1
366,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,introduction to ai fairness,"gemalmaz ma,yin m","zhang y,bellamy r,liao qv,singh m","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","algorithmic fairness,bias,decision support,discrimination-aware machine",0
367,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","gemalmaz ma,yin m","zhou n,zhang z,nair vn,singhal h,chen j","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","ai,ml,algorithm,bias,fairness",1
368,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,applying differential privacy mechanism in artificial intelligence,"gemalmaz ma,yin m","zhu t,yu ps","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
369,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"artificial intelligence, intersectionality, and the future of public health","giovanola b,tiribelli s","bauer gr,lizotte dj","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",1
370,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,a sociotechnical view of algorithmic fairness,"giovanola b,tiribelli s","dolata m,feuerriegel s,schwabe g","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
371,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,ai-enabled radio resource allocation in 5g for urllc and embb users,"giovanola b,tiribelli s","elsayed m,erol-kantarci m","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
372,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"giovanola b,tiribelli s","helberger n,araujo t,de vreese ch","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","algorithmic fairness,automated-decision making,public perceptions",0
373,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,principal fairness for human and algorithmic decision-making,"giovanola b,tiribelli s","imai k,jiang z","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
374,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"introduction to the special issue on ai fairness, trust, and ethics","giovanola b,tiribelli s","jr lp,bansal g,melville n,...","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","ai bias,ai fairness,ai trust,artificial intelligence",1
375,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,exploring bias and fairness in artificial intelligence and machine learning algorithms,"giovanola b,tiribelli s","khakurel ub,abdelmoumin g,bajracharya a,rawat db","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
376,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,ai & intellectual property: towards an articulated public domain,"giovanola b,tiribelli s",kop m,"bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
377,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,a survey on datasets for fairness-aware machine learning,"giovanola b,tiribelli s","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
378,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"giovanola b,tiribelli s","li c,xing w","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","fair ai,human-centered computing,math learning,perceived fairness",0
379,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,trustworthy ai: from principles to practices,"giovanola b,tiribelli s","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
380,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","giovanola b,tiribelli s","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","ai,fairness,machine learning,software development practices",1
381,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","giovanola b,tiribelli s","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
382,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,artificial intelligence in human resources management: challenges and a path forward,"giovanola b,tiribelli s","tambe p,cappelli p,yakubovich v","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
383,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,artificial intelligence and the public sector—applications and challenges,"giovanola b,tiribelli s","wirtz bw,weyerer jc,geyer c","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","ai applications,ai challenges,artificial intelligence,public sector",1
384,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,effective exposure amortizing for fair top-k recommendation,"giovanola b,tiribelli s","yang t,xu z,ai q","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
385,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,flexible and adaptive fairness-aware learning in non-stationary data streams,"giovanola b,tiribelli s","zhang w,zhang m,zhang j,liu z,...","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","ai fairness,flexible fairness,online classification",0
386,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","giovanola b,tiribelli s","zhou n,zhang z,nair vn,singhal h,chen j","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect","ai,ml,algorithm,bias,fairness",1
387,marrying fairness and explainability in supervised learning,transparency and explainability of ai systems: ethical guidelines in practice,"grabowicz pa,perello n,mishra a","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","ai systems,ethical guidelines,explainability,quality requirements,transparency",1
388,marrying fairness and explainability in supervised learning,"artificial intelligence, intersectionality, and the future of public health","grabowicz pa,perello n,mishra a","bauer gr,lizotte dj","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
389,marrying fairness and explainability in supervised learning,using edge cases to disentangle fairness and solidarity in ai ethics,"grabowicz pa,perello n,mishra a",brusseau j,"algorithmic fairness,discrimination,explainability,machine learning,supervised learning","artificial intelligence,ethics,fairness,philosophy,solidarity",0
390,marrying fairness and explainability in supervised learning,fairness & friends in the data science era,"grabowicz pa,perello n,mishra a","catania b,guerrini g,accinelli c","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
391,marrying fairness and explainability in supervised learning,a sociotechnical view of algorithmic fairness,"grabowicz pa,perello n,mishra a","dolata m,feuerriegel s,schwabe g","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
392,marrying fairness and explainability in supervised learning,ai-enabled radio resource allocation in 5g for urllc and embb users,"grabowicz pa,perello n,mishra a","elsayed m,erol-kantarci m","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
393,marrying fairness and explainability in supervised learning,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","grabowicz pa,perello n,mishra a","fletcher rr,nakeshimana a,...","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
394,marrying fairness and explainability in supervised learning,rawls's original position and algorithmic fairness,"grabowicz pa,perello n,mishra a",franke u,"algorithmic fairness,discrimination,explainability,machine learning,supervised learning","algorithmic fairness,original position,veil of ignorance,veil of uncertainty",0
395,marrying fairness and explainability in supervised learning,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"grabowicz pa,perello n,mishra a","giovanola b,tiribelli s","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",1
396,marrying fairness and explainability in supervised learning,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"grabowicz pa,perello n,mishra a","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",0
397,marrying fairness and explainability in supervised learning,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"grabowicz pa,perello n,mishra a","helberger n,araujo t,de vreese ch","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","algorithmic fairness,automated-decision making,public perceptions",0
398,marrying fairness and explainability in supervised learning,principal fairness for human and algorithmic decision-making,"grabowicz pa,perello n,mishra a","imai k,jiang z","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
399,marrying fairness and explainability in supervised learning,"introduction to the special issue on ai fairness, trust, and ethics","grabowicz pa,perello n,mishra a","jr lp,bansal g,melville n,...","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","ai bias,ai fairness,ai trust,artificial intelligence",0
400,marrying fairness and explainability in supervised learning,exploring bias and fairness in artificial intelligence and machine learning algorithms,"grabowicz pa,perello n,mishra a","khakurel ub,abdelmoumin g,bajracharya a,rawat db","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",0
401,marrying fairness and explainability in supervised learning,ai & intellectual property: towards an articulated public domain,"grabowicz pa,perello n,mishra a",kop m,"algorithmic fairness,discrimination,explainability,machine learning,supervised learning","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
402,marrying fairness and explainability in supervised learning,a survey on datasets for fairness-aware machine learning,"grabowicz pa,perello n,mishra a","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
403,marrying fairness and explainability in supervised learning,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"grabowicz pa,perello n,mishra a","li c,xing w","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","fair ai,human-centered computing,math learning,perceived fairness",1
404,marrying fairness and explainability in supervised learning,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"grabowicz pa,perello n,mishra a","li j,chignell textperiodcenteredm","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
405,marrying fairness and explainability in supervised learning,trustworthy ai: from principles to practices,"grabowicz pa,perello n,mishra a","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
406,marrying fairness and explainability in supervised learning,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","grabowicz pa,perello n,mishra a","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","ai,fairness,machine learning,software development practices",0
407,marrying fairness and explainability in supervised learning,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","grabowicz pa,perello n,mishra a","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
408,marrying fairness and explainability in supervised learning,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"grabowicz pa,perello n,mishra a","pessach d,shmueli e","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",0
409,marrying fairness and explainability in supervised learning,"on the relationship between explanations, fairness perceptions, and decisions","grabowicz pa,perello n,mishra a","schoeffer j,de-arteaga m,kuehl n","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance",0
410,marrying fairness and explainability in supervised learning,linear versus nonlinear allocation rules in risk sharing under financial fairness,"grabowicz pa,perello n,mishra a",schumacher jm,"algorithmic fairness,discrimination,explainability,machine learning,supervised learning","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
411,marrying fairness and explainability in supervised learning,artificial intelligence in human resources management: challenges and a path forward,"grabowicz pa,perello n,mishra a","tambe p,cappelli p,yakubovich v","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
412,marrying fairness and explainability in supervised learning,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"grabowicz pa,perello n,mishra a","timan t,grommé f","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","ai,fairness,framework,francisca grommé,ssrn,tjerk timan",0
413,marrying fairness and explainability in supervised learning,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"grabowicz pa,perello n,mishra a","undefined,ta l,cm c","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
414,marrying fairness and explainability in supervised learning,ai ethics in industry: a research framework,"grabowicz pa,perello n,mishra a","ville v,kai-kristian k,pekka a","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
415,marrying fairness and explainability in supervised learning,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"grabowicz pa,perello n,mishra a","wachter s,mittelstadt b,russell c","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
416,marrying fairness and explainability in supervised learning,user association and resource allocation in full-duplex relay aided noma systems,"grabowicz pa,perello n,mishra a","wang l,ai y,liu n,fei a","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
417,marrying fairness and explainability in supervised learning,artificial intelligence and the public sector—applications and challenges,"grabowicz pa,perello n,mishra a","wirtz bw,weyerer jc,geyer c","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","ai applications,ai challenges,artificial intelligence,public sector",1
418,marrying fairness and explainability in supervised learning,the rising tide of artificial intelligence and business automation: developing an ethical framework,"grabowicz pa,perello n,mishra a","wright sa,schultz ae","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",0
419,marrying fairness and explainability in supervised learning,achieving causal fairness through generative adversarial networks,"grabowicz pa,perello n,mishra a","xu d,wu y,yuan s,zhang l,wu x","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",1
420,marrying fairness and explainability in supervised learning,algorithmic fairness in computational medicine,"grabowicz pa,perello n,mishra a","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","algorithmic fairness,computational medicine",0
421,marrying fairness and explainability in supervised learning,effective exposure amortizing for fair top-k recommendation,"grabowicz pa,perello n,mishra a","yang t,xu z,ai q","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
422,marrying fairness and explainability in supervised learning,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","grabowicz pa,perello n,mishra a","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
423,marrying fairness and explainability in supervised learning,flexible and adaptive fairness-aware learning in non-stationary data streams,"grabowicz pa,perello n,mishra a","zhang w,zhang m,zhang j,liu z,...","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","ai fairness,flexible fairness,online classification",1
424,marrying fairness and explainability in supervised learning,introduction to ai fairness,"grabowicz pa,perello n,mishra a","zhang y,bellamy r,liao qv,singh m","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","algorithmic fairness,bias,decision support,discrimination-aware machine",0
425,marrying fairness and explainability in supervised learning,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","grabowicz pa,perello n,mishra a","zhou n,zhang z,nair vn,singhal h,chen j","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","ai,ml,algorithm,bias,fairness",0
426,marrying fairness and explainability in supervised learning,applying differential privacy mechanism in artificial intelligence,"grabowicz pa,perello n,mishra a","zhu t,yu ps","algorithmic fairness,discrimination,explainability,machine learning,supervised learning","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
427,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,transparency and explainability of ai systems: ethical guidelines in practice,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","ai systems,ethical guidelines,explainability,quality requirements,transparency",1
428,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"artificial intelligence, intersectionality, and the future of public health","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","bauer gr,lizotte dj","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
429,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,fairness & friends in the data science era,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","catania b,guerrini g,accinelli c","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
430,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,a sociotechnical view of algorithmic fairness,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","dolata m,feuerriegel s,schwabe g","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
431,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,ai-enabled radio resource allocation in 5g for urllc and embb users,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","elsayed m,erol-kantarci m","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
432,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","fletcher rr,nakeshimana a,...","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",1
433,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","giovanola b,tiribelli s","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
434,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","helberger n,araujo t,de vreese ch","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","algorithmic fairness,automated-decision making,public perceptions",1
435,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,principal fairness for human and algorithmic decision-making,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","imai k,jiang z","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
436,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"introduction to the special issue on ai fairness, trust, and ethics","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","jr lp,bansal g,melville n,...","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","ai bias,ai fairness,ai trust,artificial intelligence",1
437,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,exploring bias and fairness in artificial intelligence and machine learning algorithms,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","khakurel ub,abdelmoumin g,bajracharya a,rawat db","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
438,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,ai & intellectual property: towards an articulated public domain,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp",kop m,"artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
439,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,a survey on datasets for fairness-aware machine learning,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
440,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","li c,xing w","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","fair ai,human-centered computing,math learning,perceived fairness",0
441,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","li j,chignell textperiodcenteredm","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
442,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,trustworthy ai: from principles to practices,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
443,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","ai,fairness,machine learning,software development practices",1
444,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
445,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","pessach d,shmueli e","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",1
446,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,linear versus nonlinear allocation rules in risk sharing under financial fairness,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp",schumacher jm,"artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
447,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,artificial intelligence in human resources management: challenges and a path forward,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","tambe p,cappelli p,yakubovich v","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
448,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","undefined,ta l,cm c","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
449,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,ai ethics in industry: a research framework,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","ville v,kai-kristian k,pekka a","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
450,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","wachter s,mittelstadt b,russell c","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",1
451,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,user association and resource allocation in full-duplex relay aided noma systems,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","wang l,ai y,liu n,fei a","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
452,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,artificial intelligence and the public sector—applications and challenges,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","wirtz bw,weyerer jc,geyer c","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","ai applications,ai challenges,artificial intelligence,public sector",0
453,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,the rising tide of artificial intelligence and business automation: developing an ethical framework,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","wright sa,schultz ae","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",1
454,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,achieving causal fairness through generative adversarial networks,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","xu d,wu y,yuan s,zhang l,wu x","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
455,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,algorithmic fairness in computational medicine,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","algorithmic fairness,computational medicine",0
456,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,effective exposure amortizing for fair top-k recommendation,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","yang t,xu z,ai q","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
457,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
458,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,flexible and adaptive fairness-aware learning in non-stationary data streams,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","zhang w,zhang m,zhang j,liu z,...","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","ai fairness,flexible fairness,online classification",0
459,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,introduction to ai fairness,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","zhang y,bellamy r,liao qv,singh m","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","algorithmic fairness,bias,decision support,discrimination-aware machine",0
460,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","zhou n,zhang z,nair vn,singhal h,chen j","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","ai,ml,algorithm,bias,fairness",1
461,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,applying differential privacy mechanism in artificial intelligence,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","zhu t,yu ps","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
462,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"artificial intelligence, intersectionality, and the future of public health","helberger n,araujo t,de vreese ch","bauer gr,lizotte dj","algorithmic fairness,automated-decision making,public perceptions","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",1
463,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,a sociotechnical view of algorithmic fairness,"helberger n,araujo t,de vreese ch","dolata m,feuerriegel s,schwabe g","algorithmic fairness,automated-decision making,public perceptions","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
464,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,ai-enabled radio resource allocation in 5g for urllc and embb users,"helberger n,araujo t,de vreese ch","elsayed m,erol-kantarci m","algorithmic fairness,automated-decision making,public perceptions","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
465,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,principal fairness for human and algorithmic decision-making,"helberger n,araujo t,de vreese ch","imai k,jiang z","algorithmic fairness,automated-decision making,public perceptions","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
466,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"introduction to the special issue on ai fairness, trust, and ethics","helberger n,araujo t,de vreese ch","jr lp,bansal g,melville n,...","algorithmic fairness,automated-decision making,public perceptions","ai bias,ai fairness,ai trust,artificial intelligence",1
467,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,ai & intellectual property: towards an articulated public domain,"helberger n,araujo t,de vreese ch",kop m,"algorithmic fairness,automated-decision making,public perceptions","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
468,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,a survey on datasets for fairness-aware machine learning,"helberger n,araujo t,de vreese ch","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","algorithmic fairness,automated-decision making,public perceptions","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
469,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"helberger n,araujo t,de vreese ch","li c,xing w","algorithmic fairness,automated-decision making,public perceptions","fair ai,human-centered computing,math learning,perceived fairness",1
470,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,trustworthy ai: from principles to practices,"helberger n,araujo t,de vreese ch","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","algorithmic fairness,automated-decision making,public perceptions","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
471,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","helberger n,araujo t,de vreese ch","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","algorithmic fairness,automated-decision making,public perceptions","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
472,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,artificial intelligence in human resources management: challenges and a path forward,"helberger n,araujo t,de vreese ch","tambe p,cappelli p,yakubovich v","algorithmic fairness,automated-decision making,public perceptions","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
473,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,artificial intelligence and the public sector—applications and challenges,"helberger n,araujo t,de vreese ch","wirtz bw,weyerer jc,geyer c","algorithmic fairness,automated-decision making,public perceptions","ai applications,ai challenges,artificial intelligence,public sector",1
474,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,effective exposure amortizing for fair top-k recommendation,"helberger n,araujo t,de vreese ch","yang t,xu z,ai q","algorithmic fairness,automated-decision making,public perceptions","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
475,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,flexible and adaptive fairness-aware learning in non-stationary data streams,"helberger n,araujo t,de vreese ch","zhang w,zhang m,zhang j,liu z,...","algorithmic fairness,automated-decision making,public perceptions","ai fairness,flexible fairness,online classification",1
476,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","helberger n,araujo t,de vreese ch","zhou n,zhang z,nair vn,singhal h,chen j","algorithmic fairness,automated-decision making,public perceptions","ai,ml,algorithm,bias,fairness",1
477,principal fairness for human and algorithmic decision-making,"artificial intelligence, intersectionality, and the future of public health","imai k,jiang z","bauer gr,lizotte dj","algorithmic fairness,causal inference,potential outcomes,principal stratification","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
478,principal fairness for human and algorithmic decision-making,a sociotechnical view of algorithmic fairness,"imai k,jiang z","dolata m,feuerriegel s,schwabe g","algorithmic fairness,causal inference,potential outcomes,principal stratification","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
479,principal fairness for human and algorithmic decision-making,ai-enabled radio resource allocation in 5g for urllc and embb users,"imai k,jiang z","elsayed m,erol-kantarci m","algorithmic fairness,causal inference,potential outcomes,principal stratification","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
480,principal fairness for human and algorithmic decision-making,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"imai k,jiang z","li c,xing w","algorithmic fairness,causal inference,potential outcomes,principal stratification","fair ai,human-centered computing,math learning,perceived fairness",0
481,principal fairness for human and algorithmic decision-making,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","imai k,jiang z","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","algorithmic fairness,causal inference,potential outcomes,principal stratification","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
482,principal fairness for human and algorithmic decision-making,flexible and adaptive fairness-aware learning in non-stationary data streams,"imai k,jiang z","zhang w,zhang m,zhang j,liu z,...","algorithmic fairness,causal inference,potential outcomes,principal stratification","ai fairness,flexible fairness,online classification",1
483,principal fairness for human and algorithmic decision-making,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","imai k,jiang z","zhou n,zhang z,nair vn,singhal h,chen j","algorithmic fairness,causal inference,potential outcomes,principal stratification","ai,ml,algorithm,bias,fairness",0
484,how different groups prioritize ethical values for responsible ai,transparency and explainability of ai systems: ethical guidelines in practice,"jakesch m,buçinca z,amershi s,olteanu a","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","responsible ai,empirical ethics,value-sensitive design","ai systems,ethical guidelines,explainability,quality requirements,transparency",1
485,how different groups prioritize ethical values for responsible ai,"artificial intelligence, intersectionality, and the future of public health","jakesch m,buçinca z,amershi s,olteanu a","bauer gr,lizotte dj","responsible ai,empirical ethics,value-sensitive design","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
486,how different groups prioritize ethical values for responsible ai,a perspective on fairness in artificial intelligence,"jakesch m,buçinca z,amershi s,olteanu a",bhattacharya p,"responsible ai,empirical ethics,value-sensitive design","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws",0
487,how different groups prioritize ethical values for responsible ai,using edge cases to disentangle fairness and solidarity in ai ethics,"jakesch m,buçinca z,amershi s,olteanu a",brusseau j,"responsible ai,empirical ethics,value-sensitive design","artificial intelligence,ethics,fairness,philosophy,solidarity",1
488,how different groups prioritize ethical values for responsible ai,fairness & friends in the data science era,"jakesch m,buçinca z,amershi s,olteanu a","catania b,guerrini g,accinelli c","responsible ai,empirical ethics,value-sensitive design","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
489,how different groups prioritize ethical values for responsible ai,a sociotechnical view of algorithmic fairness,"jakesch m,buçinca z,amershi s,olteanu a","dolata m,feuerriegel s,schwabe g","responsible ai,empirical ethics,value-sensitive design","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
490,how different groups prioritize ethical values for responsible ai,ai-enabled radio resource allocation in 5g for urllc and embb users,"jakesch m,buçinca z,amershi s,olteanu a","elsayed m,erol-kantarci m","responsible ai,empirical ethics,value-sensitive design","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
491,how different groups prioritize ethical values for responsible ai,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","jakesch m,buçinca z,amershi s,olteanu a","fletcher rr,nakeshimana a,...","responsible ai,empirical ethics,value-sensitive design","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
492,how different groups prioritize ethical values for responsible ai,rawls's original position and algorithmic fairness,"jakesch m,buçinca z,amershi s,olteanu a",franke u,"responsible ai,empirical ethics,value-sensitive design","algorithmic fairness,original position,veil of ignorance,veil of uncertainty",0
493,how different groups prioritize ethical values for responsible ai,an ontology for fairness metrics,"jakesch m,buçinca z,amershi s,olteanu a","franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","responsible ai,empirical ethics,value-sensitive design","bias,fairness metric,machine learning evaluation,rdf knowledge graph",0
494,how different groups prioritize ethical values for responsible ai,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"jakesch m,buçinca z,amershi s,olteanu a","gemalmaz ma,yin m","responsible ai,empirical ethics,value-sensitive design","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention",0
495,how different groups prioritize ethical values for responsible ai,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"jakesch m,buçinca z,amershi s,olteanu a","giovanola b,tiribelli s","responsible ai,empirical ethics,value-sensitive design","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
496,how different groups prioritize ethical values for responsible ai,marrying fairness and explainability in supervised learning,"jakesch m,buçinca z,amershi s,olteanu a","grabowicz pa,perello n,mishra a","responsible ai,empirical ethics,value-sensitive design","algorithmic fairness,discrimination,explainability,machine learning,supervised learning",0
497,how different groups prioritize ethical values for responsible ai,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"jakesch m,buçinca z,amershi s,olteanu a","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","responsible ai,empirical ethics,value-sensitive design","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",0
498,how different groups prioritize ethical values for responsible ai,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"jakesch m,buçinca z,amershi s,olteanu a","helberger n,araujo t,de vreese ch","responsible ai,empirical ethics,value-sensitive design","algorithmic fairness,automated-decision making,public perceptions",0
499,how different groups prioritize ethical values for responsible ai,principal fairness for human and algorithmic decision-making,"jakesch m,buçinca z,amershi s,olteanu a","imai k,jiang z","responsible ai,empirical ethics,value-sensitive design","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
500,how different groups prioritize ethical values for responsible ai,"introduction to the special issue on ai fairness, trust, and ethics","jakesch m,buçinca z,amershi s,olteanu a","jr lp,bansal g,melville n,...","responsible ai,empirical ethics,value-sensitive design","ai bias,ai fairness,ai trust,artificial intelligence",0
501,how different groups prioritize ethical values for responsible ai,exploring bias and fairness in artificial intelligence and machine learning algorithms,"jakesch m,buçinca z,amershi s,olteanu a","khakurel ub,abdelmoumin g,bajracharya a,rawat db","responsible ai,empirical ethics,value-sensitive design","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",0
502,how different groups prioritize ethical values for responsible ai,ai & intellectual property: towards an articulated public domain,"jakesch m,buçinca z,amershi s,olteanu a",kop m,"responsible ai,empirical ethics,value-sensitive design","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
503,how different groups prioritize ethical values for responsible ai,a survey on datasets for fairness-aware machine learning,"jakesch m,buçinca z,amershi s,olteanu a","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","responsible ai,empirical ethics,value-sensitive design","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
504,how different groups prioritize ethical values for responsible ai,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"jakesch m,buçinca z,amershi s,olteanu a","li c,xing w","responsible ai,empirical ethics,value-sensitive design","fair ai,human-centered computing,math learning,perceived fairness",0
505,how different groups prioritize ethical values for responsible ai,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"jakesch m,buçinca z,amershi s,olteanu a","li j,chignell textperiodcenteredm","responsible ai,empirical ethics,value-sensitive design","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
506,how different groups prioritize ethical values for responsible ai,trustworthy ai: from principles to practices,"jakesch m,buçinca z,amershi s,olteanu a","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","responsible ai,empirical ethics,value-sensitive design","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
507,how different groups prioritize ethical values for responsible ai,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","jakesch m,buçinca z,amershi s,olteanu a","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","responsible ai,empirical ethics,value-sensitive design","ai,fairness,machine learning,software development practices",1
508,how different groups prioritize ethical values for responsible ai,assessing ai fairness in finance,"jakesch m,buçinca z,amershi s,olteanu a","mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","responsible ai,empirical ethics,value-sensitive design","artificial intelligence,bank data processing",0
509,how different groups prioritize ethical values for responsible ai,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","jakesch m,buçinca z,amershi s,olteanu a","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","responsible ai,empirical ethics,value-sensitive design","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
510,how different groups prioritize ethical values for responsible ai,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"jakesch m,buçinca z,amershi s,olteanu a","pessach d,shmueli e","responsible ai,empirical ethics,value-sensitive design","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",0
511,how different groups prioritize ethical values for responsible ai,artificial intelligence evolution: on the virtue of killing in the artificial age,"jakesch m,buçinca z,amershi s,olteanu a",puaschunder jm,"responsible ai,empirical ethics,value-sensitive design","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,",0
512,how different groups prioritize ethical values for responsible ai,"on the relationship between explanations, fairness perceptions, and decisions","jakesch m,buçinca z,amershi s,olteanu a","schoeffer j,de-arteaga m,kuehl n","responsible ai,empirical ethics,value-sensitive design","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance",0
513,how different groups prioritize ethical values for responsible ai,linear versus nonlinear allocation rules in risk sharing under financial fairness,"jakesch m,buçinca z,amershi s,olteanu a",schumacher jm,"responsible ai,empirical ethics,value-sensitive design","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
514,how different groups prioritize ethical values for responsible ai,artificial intelligence in human resources management: challenges and a path forward,"jakesch m,buçinca z,amershi s,olteanu a","tambe p,cappelli p,yakubovich v","responsible ai,empirical ethics,value-sensitive design","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
515,how different groups prioritize ethical values for responsible ai,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"jakesch m,buçinca z,amershi s,olteanu a","timan t,grommé f","responsible ai,empirical ethics,value-sensitive design","ai,fairness,framework,francisca grommé,ssrn,tjerk timan",0
516,how different groups prioritize ethical values for responsible ai,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"jakesch m,buçinca z,amershi s,olteanu a","undefined,ta l,cm c","responsible ai,empirical ethics,value-sensitive design","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
517,how different groups prioritize ethical values for responsible ai,ai ethics in industry: a research framework,"jakesch m,buçinca z,amershi s,olteanu a","ville v,kai-kristian k,pekka a","responsible ai,empirical ethics,value-sensitive design","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
518,how different groups prioritize ethical values for responsible ai,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"jakesch m,buçinca z,amershi s,olteanu a","wachter s,mittelstadt b,russell c","responsible ai,empirical ethics,value-sensitive design","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
519,how different groups prioritize ethical values for responsible ai,user association and resource allocation in full-duplex relay aided noma systems,"jakesch m,buçinca z,amershi s,olteanu a","wang l,ai y,liu n,fei a","responsible ai,empirical ethics,value-sensitive design","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
520,how different groups prioritize ethical values for responsible ai,artificial intelligence and the public sector—applications and challenges,"jakesch m,buçinca z,amershi s,olteanu a","wirtz bw,weyerer jc,geyer c","responsible ai,empirical ethics,value-sensitive design","ai applications,ai challenges,artificial intelligence,public sector",0
521,how different groups prioritize ethical values for responsible ai,the rising tide of artificial intelligence and business automation: developing an ethical framework,"jakesch m,buçinca z,amershi s,olteanu a","wright sa,schultz ae","responsible ai,empirical ethics,value-sensitive design","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",0
522,how different groups prioritize ethical values for responsible ai,achieving causal fairness through generative adversarial networks,"jakesch m,buçinca z,amershi s,olteanu a","xu d,wu y,yuan s,zhang l,wu x","responsible ai,empirical ethics,value-sensitive design","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
523,how different groups prioritize ethical values for responsible ai,algorithmic fairness in computational medicine,"jakesch m,buçinca z,amershi s,olteanu a","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","responsible ai,empirical ethics,value-sensitive design","algorithmic fairness,computational medicine",0
524,how different groups prioritize ethical values for responsible ai,effective exposure amortizing for fair top-k recommendation,"jakesch m,buçinca z,amershi s,olteanu a","yang t,xu z,ai q","responsible ai,empirical ethics,value-sensitive design","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
525,how different groups prioritize ethical values for responsible ai,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","jakesch m,buçinca z,amershi s,olteanu a","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","responsible ai,empirical ethics,value-sensitive design","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
526,how different groups prioritize ethical values for responsible ai,flexible and adaptive fairness-aware learning in non-stationary data streams,"jakesch m,buçinca z,amershi s,olteanu a","zhang w,zhang m,zhang j,liu z,...","responsible ai,empirical ethics,value-sensitive design","ai fairness,flexible fairness,online classification",0
527,how different groups prioritize ethical values for responsible ai,introduction to ai fairness,"jakesch m,buçinca z,amershi s,olteanu a","zhang y,bellamy r,liao qv,singh m","responsible ai,empirical ethics,value-sensitive design","algorithmic fairness,bias,decision support,discrimination-aware machine",0
528,how different groups prioritize ethical values for responsible ai,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","jakesch m,buçinca z,amershi s,olteanu a","zhou n,zhang z,nair vn,singhal h,chen j","responsible ai,empirical ethics,value-sensitive design","ai,ml,algorithm,bias,fairness",0
529,how different groups prioritize ethical values for responsible ai,applying differential privacy mechanism in artificial intelligence,"jakesch m,buçinca z,amershi s,olteanu a","zhu t,yu ps","responsible ai,empirical ethics,value-sensitive design","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
530,"introduction to the special issue on ai fairness, trust, and ethics","artificial intelligence, intersectionality, and the future of public health","jr lp,bansal g,melville n,...","bauer gr,lizotte dj","ai bias,ai fairness,ai trust,artificial intelligence","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
531,"introduction to the special issue on ai fairness, trust, and ethics",a sociotechnical view of algorithmic fairness,"jr lp,bansal g,melville n,...","dolata m,feuerriegel s,schwabe g","ai bias,ai fairness,ai trust,artificial intelligence","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
532,"introduction to the special issue on ai fairness, trust, and ethics",ai-enabled radio resource allocation in 5g for urllc and embb users,"jr lp,bansal g,melville n,...","elsayed m,erol-kantarci m","ai bias,ai fairness,ai trust,artificial intelligence","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
533,"introduction to the special issue on ai fairness, trust, and ethics",principal fairness for human and algorithmic decision-making,"jr lp,bansal g,melville n,...","imai k,jiang z","ai bias,ai fairness,ai trust,artificial intelligence","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
534,"introduction to the special issue on ai fairness, trust, and ethics",a survey on datasets for fairness-aware machine learning,"jr lp,bansal g,melville n,...","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","ai bias,ai fairness,ai trust,artificial intelligence","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
535,"introduction to the special issue on ai fairness, trust, and ethics",revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"jr lp,bansal g,melville n,...","li c,xing w","ai bias,ai fairness,ai trust,artificial intelligence","fair ai,human-centered computing,math learning,perceived fairness",1
536,"introduction to the special issue on ai fairness, trust, and ethics",trustworthy ai: from principles to practices,"jr lp,bansal g,melville n,...","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","ai bias,ai fairness,ai trust,artificial intelligence","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
537,"introduction to the special issue on ai fairness, trust, and ethics","ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","jr lp,bansal g,melville n,...","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","ai bias,ai fairness,ai trust,artificial intelligence","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
538,"introduction to the special issue on ai fairness, trust, and ethics",artificial intelligence in human resources management: challenges and a path forward,"jr lp,bansal g,melville n,...","tambe p,cappelli p,yakubovich v","ai bias,ai fairness,ai trust,artificial intelligence","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
539,"introduction to the special issue on ai fairness, trust, and ethics",artificial intelligence and the public sector—applications and challenges,"jr lp,bansal g,melville n,...","wirtz bw,weyerer jc,geyer c","ai bias,ai fairness,ai trust,artificial intelligence","ai applications,ai challenges,artificial intelligence,public sector",1
540,"introduction to the special issue on ai fairness, trust, and ethics",flexible and adaptive fairness-aware learning in non-stationary data streams,"jr lp,bansal g,melville n,...","zhang w,zhang m,zhang j,liu z,...","ai bias,ai fairness,ai trust,artificial intelligence","ai fairness,flexible fairness,online classification",0
541,"introduction to the special issue on ai fairness, trust, and ethics","bias, fairness and accountability with artificial intelligence and machine learning algorithms","jr lp,bansal g,melville n,...","zhou n,zhang z,nair vn,singhal h,chen j","ai bias,ai fairness,ai trust,artificial intelligence","ai,ml,algorithm,bias,fairness",1
542,exploring bias and fairness in artificial intelligence and machine learning algorithms,"artificial intelligence, intersectionality, and the future of public health","khakurel ub,abdelmoumin g,bajracharya a,rawat db","bauer gr,lizotte dj","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
543,exploring bias and fairness in artificial intelligence and machine learning algorithms,a sociotechnical view of algorithmic fairness,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","dolata m,feuerriegel s,schwabe g","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
544,exploring bias and fairness in artificial intelligence and machine learning algorithms,ai-enabled radio resource allocation in 5g for urllc and embb users,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","elsayed m,erol-kantarci m","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
545,exploring bias and fairness in artificial intelligence and machine learning algorithms,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","helberger n,araujo t,de vreese ch","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","algorithmic fairness,automated-decision making,public perceptions",0
546,exploring bias and fairness in artificial intelligence and machine learning algorithms,principal fairness for human and algorithmic decision-making,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","imai k,jiang z","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
547,exploring bias and fairness in artificial intelligence and machine learning algorithms,"introduction to the special issue on ai fairness, trust, and ethics","khakurel ub,abdelmoumin g,bajracharya a,rawat db","jr lp,bansal g,melville n,...","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","ai bias,ai fairness,ai trust,artificial intelligence",1
548,exploring bias and fairness in artificial intelligence and machine learning algorithms,ai & intellectual property: towards an articulated public domain,"khakurel ub,abdelmoumin g,bajracharya a,rawat db",kop m,"artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
549,exploring bias and fairness in artificial intelligence and machine learning algorithms,a survey on datasets for fairness-aware machine learning,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
550,exploring bias and fairness in artificial intelligence and machine learning algorithms,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","li c,xing w","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","fair ai,human-centered computing,math learning,perceived fairness",1
551,exploring bias and fairness in artificial intelligence and machine learning algorithms,trustworthy ai: from principles to practices,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
552,exploring bias and fairness in artificial intelligence and machine learning algorithms,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","khakurel ub,abdelmoumin g,bajracharya a,rawat db","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
553,exploring bias and fairness in artificial intelligence and machine learning algorithms,artificial intelligence in human resources management: challenges and a path forward,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","tambe p,cappelli p,yakubovich v","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",1
554,exploring bias and fairness in artificial intelligence and machine learning algorithms,artificial intelligence and the public sector—applications and challenges,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","wirtz bw,weyerer jc,geyer c","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","ai applications,ai challenges,artificial intelligence,public sector",1
555,exploring bias and fairness in artificial intelligence and machine learning algorithms,effective exposure amortizing for fair top-k recommendation,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","yang t,xu z,ai q","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
556,exploring bias and fairness in artificial intelligence and machine learning algorithms,flexible and adaptive fairness-aware learning in non-stationary data streams,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","zhang w,zhang m,zhang j,liu z,...","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","ai fairness,flexible fairness,online classification",0
557,exploring bias and fairness in artificial intelligence and machine learning algorithms,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","khakurel ub,abdelmoumin g,bajracharya a,rawat db","zhou n,zhang z,nair vn,singhal h,chen j","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling","ai,ml,algorithm,bias,fairness",1
558,ai & intellectual property: towards an articulated public domain,"artificial intelligence, intersectionality, and the future of public health",kop m,"bauer gr,lizotte dj","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
559,ai & intellectual property: towards an articulated public domain,a sociotechnical view of algorithmic fairness,kop m,"dolata m,feuerriegel s,schwabe g","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
560,ai & intellectual property: towards an articulated public domain,ai-enabled radio resource allocation in 5g for urllc and embb users,kop m,"elsayed m,erol-kantarci m","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
561,ai & intellectual property: towards an articulated public domain,principal fairness for human and algorithmic decision-making,kop m,"imai k,jiang z","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
562,ai & intellectual property: towards an articulated public domain,"introduction to the special issue on ai fairness, trust, and ethics",kop m,"jr lp,bansal g,melville n,...","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments","ai bias,ai fairness,ai trust,artificial intelligence",0
563,ai & intellectual property: towards an articulated public domain,a survey on datasets for fairness-aware machine learning,kop m,"le quy t,roy a,iosifidis v,zhang w,ntoutsi e","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
564,ai & intellectual property: towards an articulated public domain,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,kop m,"li c,xing w","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments","fair ai,human-centered computing,math learning,perceived fairness",0
565,ai & intellectual property: towards an articulated public domain,trustworthy ai: from principles to practices,kop m,"libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
566,ai & intellectual property: towards an articulated public domain,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox",kop m,"pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
567,ai & intellectual property: towards an articulated public domain,artificial intelligence in human resources management: challenges and a path forward,kop m,"tambe p,cappelli p,yakubovich v","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
568,ai & intellectual property: towards an articulated public domain,artificial intelligence and the public sector—applications and challenges,kop m,"wirtz bw,weyerer jc,geyer c","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments","ai applications,ai challenges,artificial intelligence,public sector",0
569,ai & intellectual property: towards an articulated public domain,flexible and adaptive fairness-aware learning in non-stationary data streams,kop m,"zhang w,zhang m,zhang j,liu z,...","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments","ai fairness,flexible fairness,online classification",0
570,ai & intellectual property: towards an articulated public domain,"bias, fairness and accountability with artificial intelligence and machine learning algorithms",kop m,"zhou n,zhang z,nair vn,singhal h,chen j","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments","ai,ml,algorithm,bias,fairness",0
571,a survey on datasets for fairness-aware machine learning,"artificial intelligence, intersectionality, and the future of public health","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","bauer gr,lizotte dj","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
572,a survey on datasets for fairness-aware machine learning,a sociotechnical view of algorithmic fairness,"le quy t,roy a,iosifidis v,zhang w,ntoutsi e","dolata m,feuerriegel s,schwabe g","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
573,a survey on datasets for fairness-aware machine learning,ai-enabled radio resource allocation in 5g for urllc and embb users,"le quy t,roy a,iosifidis v,zhang w,ntoutsi e","elsayed m,erol-kantarci m","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
574,a survey on datasets for fairness-aware machine learning,principal fairness for human and algorithmic decision-making,"le quy t,roy a,iosifidis v,zhang w,ntoutsi e","imai k,jiang z","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
575,a survey on datasets for fairness-aware machine learning,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"le quy t,roy a,iosifidis v,zhang w,ntoutsi e","li c,xing w","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness","fair ai,human-centered computing,math learning,perceived fairness",1
576,a survey on datasets for fairness-aware machine learning,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
577,a survey on datasets for fairness-aware machine learning,flexible and adaptive fairness-aware learning in non-stationary data streams,"le quy t,roy a,iosifidis v,zhang w,ntoutsi e","zhang w,zhang m,zhang j,liu z,...","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness","ai fairness,flexible fairness,online classification",1
578,a survey on datasets for fairness-aware machine learning,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","zhou n,zhang z,nair vn,singhal h,chen j","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness","ai,ml,algorithm,bias,fairness",0
579,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"artificial intelligence, intersectionality, and the future of public health","li c,xing w","bauer gr,lizotte dj","fair ai,human-centered computing,math learning,perceived fairness","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
580,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,a sociotechnical view of algorithmic fairness,"li c,xing w","dolata m,feuerriegel s,schwabe g","fair ai,human-centered computing,math learning,perceived fairness","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
581,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,ai-enabled radio resource allocation in 5g for urllc and embb users,"li c,xing w","elsayed m,erol-kantarci m","fair ai,human-centered computing,math learning,perceived fairness","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
582,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","li c,xing w","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","fair ai,human-centered computing,math learning,perceived fairness","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
583,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,flexible and adaptive fairness-aware learning in non-stationary data streams,"li c,xing w","zhang w,zhang m,zhang j,liu z,...","fair ai,human-centered computing,math learning,perceived fairness","ai fairness,flexible fairness,online classification",0
584,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,transparency and explainability of ai systems: ethical guidelines in practice,"li j,chignell textperiodcenteredm","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
585,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"artificial intelligence, intersectionality, and the future of public health","li j,chignell textperiodcenteredm","bauer gr,lizotte dj","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
586,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,fairness & friends in the data science era,"li j,chignell textperiodcenteredm","catania b,guerrini g,accinelli c","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
587,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,a sociotechnical view of algorithmic fairness,"li j,chignell textperiodcenteredm","dolata m,feuerriegel s,schwabe g","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
588,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,ai-enabled radio resource allocation in 5g for urllc and embb users,"li j,chignell textperiodcenteredm","elsayed m,erol-kantarci m","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
589,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","li j,chignell textperiodcenteredm","fletcher rr,nakeshimana a,...","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
590,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"li j,chignell textperiodcenteredm","giovanola b,tiribelli s","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",1
591,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"li j,chignell textperiodcenteredm","helberger n,araujo t,de vreese ch","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","algorithmic fairness,automated-decision making,public perceptions",1
592,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,principal fairness for human and algorithmic decision-making,"li j,chignell textperiodcenteredm","imai k,jiang z","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
593,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"introduction to the special issue on ai fairness, trust, and ethics","li j,chignell textperiodcenteredm","jr lp,bansal g,melville n,...","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","ai bias,ai fairness,ai trust,artificial intelligence",1
594,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,exploring bias and fairness in artificial intelligence and machine learning algorithms,"li j,chignell textperiodcenteredm","khakurel ub,abdelmoumin g,bajracharya a,rawat db","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
595,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,ai & intellectual property: towards an articulated public domain,"li j,chignell textperiodcenteredm",kop m,"artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
596,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,a survey on datasets for fairness-aware machine learning,"li j,chignell textperiodcenteredm","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
597,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"li j,chignell textperiodcenteredm","li c,xing w","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","fair ai,human-centered computing,math learning,perceived fairness",0
598,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,trustworthy ai: from principles to practices,"li j,chignell textperiodcenteredm","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
599,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","li j,chignell textperiodcenteredm","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","ai,fairness,machine learning,software development practices",1
600,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","li j,chignell textperiodcenteredm","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
601,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"li j,chignell textperiodcenteredm","pessach d,shmueli e","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",0
602,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,artificial intelligence in human resources management: challenges and a path forward,"li j,chignell textperiodcenteredm","tambe p,cappelli p,yakubovich v","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
603,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,ai ethics in industry: a research framework,"li j,chignell textperiodcenteredm","ville v,kai-kristian k,pekka a","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
604,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"li j,chignell textperiodcenteredm","wachter s,mittelstadt b,russell c","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",1
605,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,artificial intelligence and the public sector—applications and challenges,"li j,chignell textperiodcenteredm","wirtz bw,weyerer jc,geyer c","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","ai applications,ai challenges,artificial intelligence,public sector",0
606,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,algorithmic fairness in computational medicine,"li j,chignell textperiodcenteredm","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","algorithmic fairness,computational medicine",0
607,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,effective exposure amortizing for fair top-k recommendation,"li j,chignell textperiodcenteredm","yang t,xu z,ai q","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
608,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,flexible and adaptive fairness-aware learning in non-stationary data streams,"li j,chignell textperiodcenteredm","zhang w,zhang m,zhang j,liu z,...","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","ai fairness,flexible fairness,online classification",0
609,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","li j,chignell textperiodcenteredm","zhou n,zhang z,nair vn,singhal h,chen j","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","ai,ml,algorithm,bias,fairness",1
610,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,applying differential privacy mechanism in artificial intelligence,"li j,chignell textperiodcenteredm","zhu t,yu ps","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
611,trustworthy ai: from principles to practices,"artificial intelligence, intersectionality, and the future of public health","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","bauer gr,lizotte dj","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
612,trustworthy ai: from principles to practices,a sociotechnical view of algorithmic fairness,"libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","dolata m,feuerriegel s,schwabe g","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
613,trustworthy ai: from principles to practices,ai-enabled radio resource allocation in 5g for urllc and embb users,"libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","elsayed m,erol-kantarci m","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
614,trustworthy ai: from principles to practices,principal fairness for human and algorithmic decision-making,"libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","imai k,jiang z","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
615,trustworthy ai: from principles to practices,a survey on datasets for fairness-aware machine learning,"libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
616,trustworthy ai: from principles to practices,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","li c,xing w","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency","fair ai,human-centered computing,math learning,perceived fairness",0
617,trustworthy ai: from principles to practices,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
618,trustworthy ai: from principles to practices,flexible and adaptive fairness-aware learning in non-stationary data streams,"libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","zhang w,zhang m,zhang j,liu z,...","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency","ai fairness,flexible fairness,online classification",0
619,trustworthy ai: from principles to practices,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","zhou n,zhang z,nair vn,singhal h,chen j","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency","ai,ml,algorithm,bias,fairness",0
620,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","artificial intelligence, intersectionality, and the future of public health","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","bauer gr,lizotte dj","ai,fairness,machine learning,software development practices","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
621,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",a sociotechnical view of algorithmic fairness,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","dolata m,feuerriegel s,schwabe g","ai,fairness,machine learning,software development practices","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
622,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",ai-enabled radio resource allocation in 5g for urllc and embb users,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","elsayed m,erol-kantarci m","ai,fairness,machine learning,software development practices","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
623,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","helberger n,araujo t,de vreese ch","ai,fairness,machine learning,software development practices","algorithmic fairness,automated-decision making,public perceptions",1
624,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",principal fairness for human and algorithmic decision-making,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","imai k,jiang z","ai,fairness,machine learning,software development practices","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
625,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","introduction to the special issue on ai fairness, trust, and ethics","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","jr lp,bansal g,melville n,...","ai,fairness,machine learning,software development practices","ai bias,ai fairness,ai trust,artificial intelligence",0
626,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",exploring bias and fairness in artificial intelligence and machine learning algorithms,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","khakurel ub,abdelmoumin g,bajracharya a,rawat db","ai,fairness,machine learning,software development practices","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
627,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",ai & intellectual property: towards an articulated public domain,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h",kop m,"ai,fairness,machine learning,software development practices","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
628,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",a survey on datasets for fairness-aware machine learning,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","ai,fairness,machine learning,software development practices","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
629,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","li c,xing w","ai,fairness,machine learning,software development practices","fair ai,human-centered computing,math learning,perceived fairness",0
630,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",trustworthy ai: from principles to practices,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","ai,fairness,machine learning,software development practices","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
631,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","ai,fairness,machine learning,software development practices","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
632,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",artificial intelligence in human resources management: challenges and a path forward,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","tambe p,cappelli p,yakubovich v","ai,fairness,machine learning,software development practices","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",1
633,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",artificial intelligence and the public sector—applications and challenges,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","wirtz bw,weyerer jc,geyer c","ai,fairness,machine learning,software development practices","ai applications,ai challenges,artificial intelligence,public sector",1
634,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",effective exposure amortizing for fair top-k recommendation,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","yang t,xu z,ai q","ai,fairness,machine learning,software development practices","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
635,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",flexible and adaptive fairness-aware learning in non-stationary data streams,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","zhang w,zhang m,zhang j,liu z,...","ai,fairness,machine learning,software development practices","ai fairness,flexible fairness,online classification",0
636,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","bias, fairness and accountability with artificial intelligence and machine learning algorithms","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","zhou n,zhang z,nair vn,singhal h,chen j","ai,fairness,machine learning,software development practices","ai,ml,algorithm,bias,fairness",0
637,assessing ai fairness in finance,transparency and explainability of ai systems: ethical guidelines in practice,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","artificial intelligence,bank data processing","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
638,assessing ai fairness in finance,"artificial intelligence, intersectionality, and the future of public health","mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","bauer gr,lizotte dj","artificial intelligence,bank data processing","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
639,assessing ai fairness in finance,using edge cases to disentangle fairness and solidarity in ai ethics,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy",brusseau j,"artificial intelligence,bank data processing","artificial intelligence,ethics,fairness,philosophy,solidarity",0
640,assessing ai fairness in finance,fairness & friends in the data science era,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","catania b,guerrini g,accinelli c","artificial intelligence,bank data processing","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",1
641,assessing ai fairness in finance,a sociotechnical view of algorithmic fairness,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","dolata m,feuerriegel s,schwabe g","artificial intelligence,bank data processing","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
642,assessing ai fairness in finance,ai-enabled radio resource allocation in 5g for urllc and embb users,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","elsayed m,erol-kantarci m","artificial intelligence,bank data processing","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
643,assessing ai fairness in finance,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","fletcher rr,nakeshimana a,...","artificial intelligence,bank data processing","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
644,assessing ai fairness in finance,rawls's original position and algorithmic fairness,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy",franke u,"artificial intelligence,bank data processing","algorithmic fairness,original position,veil of ignorance,veil of uncertainty",0
645,assessing ai fairness in finance,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","gemalmaz ma,yin m","artificial intelligence,bank data processing","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention",0
646,assessing ai fairness in finance,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","giovanola b,tiribelli s","artificial intelligence,bank data processing","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
647,assessing ai fairness in finance,marrying fairness and explainability in supervised learning,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","grabowicz pa,perello n,mishra a","artificial intelligence,bank data processing","algorithmic fairness,discrimination,explainability,machine learning,supervised learning",0
648,assessing ai fairness in finance,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","artificial intelligence,bank data processing","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",0
649,assessing ai fairness in finance,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","helberger n,araujo t,de vreese ch","artificial intelligence,bank data processing","algorithmic fairness,automated-decision making,public perceptions",0
650,assessing ai fairness in finance,principal fairness for human and algorithmic decision-making,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","imai k,jiang z","artificial intelligence,bank data processing","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
651,assessing ai fairness in finance,"introduction to the special issue on ai fairness, trust, and ethics","mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","jr lp,bansal g,melville n,...","artificial intelligence,bank data processing","ai bias,ai fairness,ai trust,artificial intelligence",1
652,assessing ai fairness in finance,exploring bias and fairness in artificial intelligence and machine learning algorithms,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","khakurel ub,abdelmoumin g,bajracharya a,rawat db","artificial intelligence,bank data processing","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
653,assessing ai fairness in finance,ai & intellectual property: towards an articulated public domain,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy",kop m,"artificial intelligence,bank data processing","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
654,assessing ai fairness in finance,a survey on datasets for fairness-aware machine learning,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","artificial intelligence,bank data processing","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
655,assessing ai fairness in finance,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","li c,xing w","artificial intelligence,bank data processing","fair ai,human-centered computing,math learning,perceived fairness",0
656,assessing ai fairness in finance,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","li j,chignell textperiodcenteredm","artificial intelligence,bank data processing","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
657,assessing ai fairness in finance,trustworthy ai: from principles to practices,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","artificial intelligence,bank data processing","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
658,assessing ai fairness in finance,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","artificial intelligence,bank data processing","ai,fairness,machine learning,software development practices",1
659,assessing ai fairness in finance,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","artificial intelligence,bank data processing","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
660,assessing ai fairness in finance,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","pessach d,shmueli e","artificial intelligence,bank data processing","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",0
661,assessing ai fairness in finance,"on the relationship between explanations, fairness perceptions, and decisions","mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","schoeffer j,de-arteaga m,kuehl n","artificial intelligence,bank data processing","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance",0
662,assessing ai fairness in finance,linear versus nonlinear allocation rules in risk sharing under financial fairness,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy",schumacher jm,"artificial intelligence,bank data processing","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
663,assessing ai fairness in finance,artificial intelligence in human resources management: challenges and a path forward,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","tambe p,cappelli p,yakubovich v","artificial intelligence,bank data processing","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
664,assessing ai fairness in finance,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","timan t,grommé f","artificial intelligence,bank data processing","ai,fairness,framework,francisca grommé,ssrn,tjerk timan",0
665,assessing ai fairness in finance,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","undefined,ta l,cm c","artificial intelligence,bank data processing","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
666,assessing ai fairness in finance,ai ethics in industry: a research framework,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","ville v,kai-kristian k,pekka a","artificial intelligence,bank data processing","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
667,assessing ai fairness in finance,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","wachter s,mittelstadt b,russell c","artificial intelligence,bank data processing","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
668,assessing ai fairness in finance,user association and resource allocation in full-duplex relay aided noma systems,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","wang l,ai y,liu n,fei a","artificial intelligence,bank data processing","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
669,assessing ai fairness in finance,artificial intelligence and the public sector—applications and challenges,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","wirtz bw,weyerer jc,geyer c","artificial intelligence,bank data processing","ai applications,ai challenges,artificial intelligence,public sector",0
670,assessing ai fairness in finance,the rising tide of artificial intelligence and business automation: developing an ethical framework,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","wright sa,schultz ae","artificial intelligence,bank data processing","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",0
671,assessing ai fairness in finance,achieving causal fairness through generative adversarial networks,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","xu d,wu y,yuan s,zhang l,wu x","artificial intelligence,bank data processing","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
672,assessing ai fairness in finance,algorithmic fairness in computational medicine,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","artificial intelligence,bank data processing","algorithmic fairness,computational medicine",1
673,assessing ai fairness in finance,effective exposure amortizing for fair top-k recommendation,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","yang t,xu z,ai q","artificial intelligence,bank data processing","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
674,assessing ai fairness in finance,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","artificial intelligence,bank data processing","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
675,assessing ai fairness in finance,flexible and adaptive fairness-aware learning in non-stationary data streams,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","zhang w,zhang m,zhang j,liu z,...","artificial intelligence,bank data processing","ai fairness,flexible fairness,online classification",1
676,assessing ai fairness in finance,introduction to ai fairness,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","zhang y,bellamy r,liao qv,singh m","artificial intelligence,bank data processing","algorithmic fairness,bias,decision support,discrimination-aware machine",1
677,assessing ai fairness in finance,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","zhou n,zhang z,nair vn,singhal h,chen j","artificial intelligence,bank data processing","ai,ml,algorithm,bias,fairness",0
678,assessing ai fairness in finance,applying differential privacy mechanism in artificial intelligence,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","zhu t,yu ps","artificial intelligence,bank data processing","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
679,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","artificial intelligence, intersectionality, and the future of public health","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","bauer gr,lizotte dj","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
680,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,transparency and explainability of ai systems: ethical guidelines in practice,"pessach d,shmueli e","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
681,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"artificial intelligence, intersectionality, and the future of public health","pessach d,shmueli e","bauer gr,lizotte dj","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
682,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,fairness & friends in the data science era,"pessach d,shmueli e","catania b,guerrini g,accinelli c","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
683,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,a sociotechnical view of algorithmic fairness,"pessach d,shmueli e","dolata m,feuerriegel s,schwabe g","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
684,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,ai-enabled radio resource allocation in 5g for urllc and embb users,"pessach d,shmueli e","elsayed m,erol-kantarci m","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
685,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","pessach d,shmueli e","fletcher rr,nakeshimana a,...","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",1
686,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"pessach d,shmueli e","giovanola b,tiribelli s","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",1
687,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"pessach d,shmueli e","helberger n,araujo t,de vreese ch","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","algorithmic fairness,automated-decision making,public perceptions",0
688,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,principal fairness for human and algorithmic decision-making,"pessach d,shmueli e","imai k,jiang z","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
689,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"introduction to the special issue on ai fairness, trust, and ethics","pessach d,shmueli e","jr lp,bansal g,melville n,...","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","ai bias,ai fairness,ai trust,artificial intelligence",1
690,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,exploring bias and fairness in artificial intelligence and machine learning algorithms,"pessach d,shmueli e","khakurel ub,abdelmoumin g,bajracharya a,rawat db","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
691,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,ai & intellectual property: towards an articulated public domain,"pessach d,shmueli e",kop m,"algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
692,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,a survey on datasets for fairness-aware machine learning,"pessach d,shmueli e","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
693,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"pessach d,shmueli e","li c,xing w","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","fair ai,human-centered computing,math learning,perceived fairness",1
694,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,trustworthy ai: from principles to practices,"pessach d,shmueli e","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
695,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","pessach d,shmueli e","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","ai,fairness,machine learning,software development practices",1
696,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","pessach d,shmueli e","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
697,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,artificial intelligence in human resources management: challenges and a path forward,"pessach d,shmueli e","tambe p,cappelli p,yakubovich v","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
698,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,ai ethics in industry: a research framework,"pessach d,shmueli e","ville v,kai-kristian k,pekka a","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
699,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,artificial intelligence and the public sector—applications and challenges,"pessach d,shmueli e","wirtz bw,weyerer jc,geyer c","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","ai applications,ai challenges,artificial intelligence,public sector",0
700,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,effective exposure amortizing for fair top-k recommendation,"pessach d,shmueli e","yang t,xu z,ai q","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
701,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,flexible and adaptive fairness-aware learning in non-stationary data streams,"pessach d,shmueli e","zhang w,zhang m,zhang j,liu z,...","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","ai fairness,flexible fairness,online classification",1
702,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","pessach d,shmueli e","zhou n,zhang z,nair vn,singhal h,chen j","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","ai,ml,algorithm,bias,fairness",1
703,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,applying differential privacy mechanism in artificial intelligence,"pessach d,shmueli e","zhu t,yu ps","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
704,artificial intelligence evolution: on the virtue of killing in the artificial age,transparency and explainability of ai systems: ethical guidelines in practice,puaschunder jm,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","ai systems,ethical guidelines,explainability,quality requirements,transparency",1
705,artificial intelligence evolution: on the virtue of killing in the artificial age,"artificial intelligence, intersectionality, and the future of public health",puaschunder jm,"bauer gr,lizotte dj","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",1
706,artificial intelligence evolution: on the virtue of killing in the artificial age,using edge cases to disentangle fairness and solidarity in ai ethics,puaschunder jm,brusseau j,"ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","artificial intelligence,ethics,fairness,philosophy,solidarity",0
707,artificial intelligence evolution: on the virtue of killing in the artificial age,fairness & friends in the data science era,puaschunder jm,"catania b,guerrini g,accinelli c","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
708,artificial intelligence evolution: on the virtue of killing in the artificial age,a sociotechnical view of algorithmic fairness,puaschunder jm,"dolata m,feuerriegel s,schwabe g","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
709,artificial intelligence evolution: on the virtue of killing in the artificial age,ai-enabled radio resource allocation in 5g for urllc and embb users,puaschunder jm,"elsayed m,erol-kantarci m","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
710,artificial intelligence evolution: on the virtue of killing in the artificial age,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",puaschunder jm,"fletcher rr,nakeshimana a,...","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",1
711,artificial intelligence evolution: on the virtue of killing in the artificial age,rawls's original position and algorithmic fairness,puaschunder jm,franke u,"ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","algorithmic fairness,original position,veil of ignorance,veil of uncertainty",0
712,artificial intelligence evolution: on the virtue of killing in the artificial age,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,puaschunder jm,"gemalmaz ma,yin m","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention",0
713,artificial intelligence evolution: on the virtue of killing in the artificial age,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,puaschunder jm,"giovanola b,tiribelli s","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
714,artificial intelligence evolution: on the virtue of killing in the artificial age,marrying fairness and explainability in supervised learning,puaschunder jm,"grabowicz pa,perello n,mishra a","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","algorithmic fairness,discrimination,explainability,machine learning,supervised learning",0
715,artificial intelligence evolution: on the virtue of killing in the artificial age,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,puaschunder jm,"hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",1
716,artificial intelligence evolution: on the virtue of killing in the artificial age,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,puaschunder jm,"helberger n,araujo t,de vreese ch","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","algorithmic fairness,automated-decision making,public perceptions",0
717,artificial intelligence evolution: on the virtue of killing in the artificial age,principal fairness for human and algorithmic decision-making,puaschunder jm,"imai k,jiang z","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
718,artificial intelligence evolution: on the virtue of killing in the artificial age,"introduction to the special issue on ai fairness, trust, and ethics",puaschunder jm,"jr lp,bansal g,melville n,...","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","ai bias,ai fairness,ai trust,artificial intelligence",1
719,artificial intelligence evolution: on the virtue of killing in the artificial age,exploring bias and fairness in artificial intelligence and machine learning algorithms,puaschunder jm,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
720,artificial intelligence evolution: on the virtue of killing in the artificial age,ai & intellectual property: towards an articulated public domain,puaschunder jm,kop m,"ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
721,artificial intelligence evolution: on the virtue of killing in the artificial age,a survey on datasets for fairness-aware machine learning,puaschunder jm,"le quy t,roy a,iosifidis v,zhang w,ntoutsi e","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
722,artificial intelligence evolution: on the virtue of killing in the artificial age,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,puaschunder jm,"li c,xing w","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","fair ai,human-centered computing,math learning,perceived fairness",0
723,artificial intelligence evolution: on the virtue of killing in the artificial age,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,puaschunder jm,"li j,chignell textperiodcenteredm","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
724,artificial intelligence evolution: on the virtue of killing in the artificial age,trustworthy ai: from principles to practices,puaschunder jm,"libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
725,artificial intelligence evolution: on the virtue of killing in the artificial age,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",puaschunder jm,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","ai,fairness,machine learning,software development practices",0
726,artificial intelligence evolution: on the virtue of killing in the artificial age,assessing ai fairness in finance,puaschunder jm,"mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","artificial intelligence,bank data processing",0
727,artificial intelligence evolution: on the virtue of killing in the artificial age,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox",puaschunder jm,"pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
728,artificial intelligence evolution: on the virtue of killing in the artificial age,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,puaschunder jm,"pessach d,shmueli e","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",1
729,artificial intelligence evolution: on the virtue of killing in the artificial age,"on the relationship between explanations, fairness perceptions, and decisions",puaschunder jm,"schoeffer j,de-arteaga m,kuehl n","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance",0
730,artificial intelligence evolution: on the virtue of killing in the artificial age,linear versus nonlinear allocation rules in risk sharing under financial fairness,puaschunder jm,schumacher jm,"ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
731,artificial intelligence evolution: on the virtue of killing in the artificial age,artificial intelligence in human resources management: challenges and a path forward,puaschunder jm,"tambe p,cappelli p,yakubovich v","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",1
732,artificial intelligence evolution: on the virtue of killing in the artificial age,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,puaschunder jm,"timan t,grommé f","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","ai,fairness,framework,francisca grommé,ssrn,tjerk timan",0
733,artificial intelligence evolution: on the virtue of killing in the artificial age,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,puaschunder jm,"undefined,ta l,cm c","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
734,artificial intelligence evolution: on the virtue of killing in the artificial age,ai ethics in industry: a research framework,puaschunder jm,"ville v,kai-kristian k,pekka a","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
735,artificial intelligence evolution: on the virtue of killing in the artificial age,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,puaschunder jm,"wachter s,mittelstadt b,russell c","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
736,artificial intelligence evolution: on the virtue of killing in the artificial age,user association and resource allocation in full-duplex relay aided noma systems,puaschunder jm,"wang l,ai y,liu n,fei a","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
737,artificial intelligence evolution: on the virtue of killing in the artificial age,artificial intelligence and the public sector—applications and challenges,puaschunder jm,"wirtz bw,weyerer jc,geyer c","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","ai applications,ai challenges,artificial intelligence,public sector",1
738,artificial intelligence evolution: on the virtue of killing in the artificial age,the rising tide of artificial intelligence and business automation: developing an ethical framework,puaschunder jm,"wright sa,schultz ae","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",1
739,artificial intelligence evolution: on the virtue of killing in the artificial age,achieving causal fairness through generative adversarial networks,puaschunder jm,"xu d,wu y,yuan s,zhang l,wu x","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
740,artificial intelligence evolution: on the virtue of killing in the artificial age,algorithmic fairness in computational medicine,puaschunder jm,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","algorithmic fairness,computational medicine",0
741,artificial intelligence evolution: on the virtue of killing in the artificial age,effective exposure amortizing for fair top-k recommendation,puaschunder jm,"yang t,xu z,ai q","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
742,artificial intelligence evolution: on the virtue of killing in the artificial age,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",puaschunder jm,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
743,artificial intelligence evolution: on the virtue of killing in the artificial age,flexible and adaptive fairness-aware learning in non-stationary data streams,puaschunder jm,"zhang w,zhang m,zhang j,liu z,...","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","ai fairness,flexible fairness,online classification",1
744,artificial intelligence evolution: on the virtue of killing in the artificial age,introduction to ai fairness,puaschunder jm,"zhang y,bellamy r,liao qv,singh m","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","algorithmic fairness,bias,decision support,discrimination-aware machine",0
745,artificial intelligence evolution: on the virtue of killing in the artificial age,"bias, fairness and accountability with artificial intelligence and machine learning algorithms",puaschunder jm,"zhou n,zhang z,nair vn,singhal h,chen j","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","ai,ml,algorithm,bias,fairness",0
746,artificial intelligence evolution: on the virtue of killing in the artificial age,applying differential privacy mechanism in artificial intelligence,puaschunder jm,"zhu t,yu ps","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",1
747,how self-perceived reputation affects fairness towards humans and artificial intelligence,transparency and explainability of ai systems: ethical guidelines in practice,"russo pa,duradoni m,guazzini a","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","artificial intelligence,fairness,reputation,ultimatum game","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
748,how self-perceived reputation affects fairness towards humans and artificial intelligence,"artificial intelligence, intersectionality, and the future of public health","russo pa,duradoni m,guazzini a","bauer gr,lizotte dj","artificial intelligence,fairness,reputation,ultimatum game","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",1
749,how self-perceived reputation affects fairness towards humans and artificial intelligence,a perspective on fairness in artificial intelligence,"russo pa,duradoni m,guazzini a",bhattacharya p,"artificial intelligence,fairness,reputation,ultimatum game","a perspective on fairness in artificial intelligence,priyanka bhattacharya,ssrn,artificial intelligence,biases,consumer information,fairness,privacy laws",1
750,how self-perceived reputation affects fairness towards humans and artificial intelligence,using edge cases to disentangle fairness and solidarity in ai ethics,"russo pa,duradoni m,guazzini a",brusseau j,"artificial intelligence,fairness,reputation,ultimatum game","artificial intelligence,ethics,fairness,philosophy,solidarity",0
751,how self-perceived reputation affects fairness towards humans and artificial intelligence,fairness & friends in the data science era,"russo pa,duradoni m,guazzini a","catania b,guerrini g,accinelli c","artificial intelligence,fairness,reputation,ultimatum game","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",1
752,how self-perceived reputation affects fairness towards humans and artificial intelligence,bias and fairness in machine learning and artificial intelligence,"russo pa,duradoni m,guazzini a","cirillo d,rementeria mj","artificial intelligence,fairness,reputation,ultimatum game","artificial intelligence,bias,fairness,model development",1
753,how self-perceived reputation affects fairness towards humans and artificial intelligence,a sociotechnical view of algorithmic fairness,"russo pa,duradoni m,guazzini a","dolata m,feuerriegel s,schwabe g","artificial intelligence,fairness,reputation,ultimatum game","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
754,how self-perceived reputation affects fairness towards humans and artificial intelligence,ai-enabled radio resource allocation in 5g for urllc and embb users,"russo pa,duradoni m,guazzini a","elsayed m,erol-kantarci m","artificial intelligence,fairness,reputation,ultimatum game","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
755,how self-perceived reputation affects fairness towards humans and artificial intelligence,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","russo pa,duradoni m,guazzini a","fletcher rr,nakeshimana a,...","artificial intelligence,fairness,reputation,ultimatum game","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",1
756,how self-perceived reputation affects fairness towards humans and artificial intelligence,rawls's original position and algorithmic fairness,"russo pa,duradoni m,guazzini a",franke u,"artificial intelligence,fairness,reputation,ultimatum game","algorithmic fairness,original position,veil of ignorance,veil of uncertainty",1
757,how self-perceived reputation affects fairness towards humans and artificial intelligence,an ontology for fairness metrics,"russo pa,duradoni m,guazzini a","franklin js,bhanot k,ghalwash m,bennett kp,mccusker j,mcguinness dl","artificial intelligence,fairness,reputation,ultimatum game","bias,fairness metric,machine learning evaluation,rdf knowledge graph",0
758,how self-perceived reputation affects fairness towards humans and artificial intelligence,understanding decision subjects' fairness perceptions and retention in repeated interactions with ai-based decision systems,"russo pa,duradoni m,guazzini a","gemalmaz ma,yin m","artificial intelligence,fairness,reputation,ultimatum game","ai-based decision systems,fairness,fairness perceptions,human-ai interaction,human-subject experiments,retention",0
759,how self-perceived reputation affects fairness towards humans and artificial intelligence,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"russo pa,duradoni m,guazzini a","giovanola b,tiribelli s","artificial intelligence,fairness,reputation,ultimatum game","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",1
760,how self-perceived reputation affects fairness towards humans and artificial intelligence,marrying fairness and explainability in supervised learning,"russo pa,duradoni m,guazzini a","grabowicz pa,perello n,mishra a","artificial intelligence,fairness,reputation,ultimatum game","algorithmic fairness,discrimination,explainability,machine learning,supervised learning",1
761,how self-perceived reputation affects fairness towards humans and artificial intelligence,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"russo pa,duradoni m,guazzini a","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","artificial intelligence,fairness,reputation,ultimatum game","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",1
762,how self-perceived reputation affects fairness towards humans and artificial intelligence,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"russo pa,duradoni m,guazzini a","helberger n,araujo t,de vreese ch","artificial intelligence,fairness,reputation,ultimatum game","algorithmic fairness,automated-decision making,public perceptions",0
763,how self-perceived reputation affects fairness towards humans and artificial intelligence,principal fairness for human and algorithmic decision-making,"russo pa,duradoni m,guazzini a","imai k,jiang z","artificial intelligence,fairness,reputation,ultimatum game","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
764,how self-perceived reputation affects fairness towards humans and artificial intelligence,how different groups prioritize ethical values for responsible ai,"russo pa,duradoni m,guazzini a","jakesch m,buçinca z,amershi s,olteanu a","artificial intelligence,fairness,reputation,ultimatum game","responsible ai,empirical ethics,value-sensitive design",0
765,how self-perceived reputation affects fairness towards humans and artificial intelligence,"introduction to the special issue on ai fairness, trust, and ethics","russo pa,duradoni m,guazzini a","jr lp,bansal g,melville n,...","artificial intelligence,fairness,reputation,ultimatum game","ai bias,ai fairness,ai trust,artificial intelligence",0
766,how self-perceived reputation affects fairness towards humans and artificial intelligence,exploring bias and fairness in artificial intelligence and machine learning algorithms,"russo pa,duradoni m,guazzini a","khakurel ub,abdelmoumin g,bajracharya a,rawat db","artificial intelligence,fairness,reputation,ultimatum game","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
767,how self-perceived reputation affects fairness towards humans and artificial intelligence,ai & intellectual property: towards an articulated public domain,"russo pa,duradoni m,guazzini a",kop m,"artificial intelligence,fairness,reputation,ultimatum game","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",1
768,how self-perceived reputation affects fairness towards humans and artificial intelligence,a survey on datasets for fairness-aware machine learning,"russo pa,duradoni m,guazzini a","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","artificial intelligence,fairness,reputation,ultimatum game","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
769,how self-perceived reputation affects fairness towards humans and artificial intelligence,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"russo pa,duradoni m,guazzini a","li c,xing w","artificial intelligence,fairness,reputation,ultimatum game","fair ai,human-centered computing,math learning,perceived fairness",0
770,how self-perceived reputation affects fairness towards humans and artificial intelligence,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"russo pa,duradoni m,guazzini a","li j,chignell textperiodcenteredm","artificial intelligence,fairness,reputation,ultimatum game","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
771,how self-perceived reputation affects fairness towards humans and artificial intelligence,trustworthy ai: from principles to practices,"russo pa,duradoni m,guazzini a","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","artificial intelligence,fairness,reputation,ultimatum game","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
772,how self-perceived reputation affects fairness towards humans and artificial intelligence,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","russo pa,duradoni m,guazzini a","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","artificial intelligence,fairness,reputation,ultimatum game","ai,fairness,machine learning,software development practices",0
773,how self-perceived reputation affects fairness towards humans and artificial intelligence,assessing ai fairness in finance,"russo pa,duradoni m,guazzini a","mccalman l,steinberg d,abuhamad g,brunet me,williamson rc,zemel r,lin hy","artificial intelligence,fairness,reputation,ultimatum game","artificial intelligence,bank data processing",0
774,how self-perceived reputation affects fairness towards humans and artificial intelligence,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","russo pa,duradoni m,guazzini a","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","artificial intelligence,fairness,reputation,ultimatum game","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
775,how self-perceived reputation affects fairness towards humans and artificial intelligence,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"russo pa,duradoni m,guazzini a","pessach d,shmueli e","artificial intelligence,fairness,reputation,ultimatum game","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",1
776,how self-perceived reputation affects fairness towards humans and artificial intelligence,artificial intelligence evolution: on the virtue of killing in the artificial age,"russo pa,duradoni m,guazzini a",puaschunder jm,"artificial intelligence,fairness,reputation,ultimatum game","ai,ai-evolution,algorithms,cognitive robotics,emerging technologies,ethical issues,",0
777,how self-perceived reputation affects fairness towards humans and artificial intelligence,"on the relationship between explanations, fairness perceptions, and decisions","russo pa,duradoni m,guazzini a","schoeffer j,de-arteaga m,kuehl n","artificial intelligence,fairness,reputation,ultimatum game","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance",0
778,how self-perceived reputation affects fairness towards humans and artificial intelligence,linear versus nonlinear allocation rules in risk sharing under financial fairness,"russo pa,duradoni m,guazzini a",schumacher jm,"artificial intelligence,fairness,reputation,ultimatum game","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
779,how self-perceived reputation affects fairness towards humans and artificial intelligence,artificial intelligence in human resources management: challenges and a path forward,"russo pa,duradoni m,guazzini a","tambe p,cappelli p,yakubovich v","artificial intelligence,fairness,reputation,ultimatum game","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",1
780,how self-perceived reputation affects fairness towards humans and artificial intelligence,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"russo pa,duradoni m,guazzini a","timan t,grommé f","artificial intelligence,fairness,reputation,ultimatum game","ai,fairness,framework,francisca grommé,ssrn,tjerk timan",0
781,how self-perceived reputation affects fairness towards humans and artificial intelligence,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"russo pa,duradoni m,guazzini a","undefined,ta l,cm c","artificial intelligence,fairness,reputation,ultimatum game","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
782,how self-perceived reputation affects fairness towards humans and artificial intelligence,ai ethics in industry: a research framework,"russo pa,duradoni m,guazzini a","ville v,kai-kristian k,pekka a","artificial intelligence,fairness,reputation,ultimatum game","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
783,how self-perceived reputation affects fairness towards humans and artificial intelligence,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"russo pa,duradoni m,guazzini a","wachter s,mittelstadt b,russell c","artificial intelligence,fairness,reputation,ultimatum game","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
784,how self-perceived reputation affects fairness towards humans and artificial intelligence,user association and resource allocation in full-duplex relay aided noma systems,"russo pa,duradoni m,guazzini a","wang l,ai y,liu n,fei a","artificial intelligence,fairness,reputation,ultimatum game","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
785,how self-perceived reputation affects fairness towards humans and artificial intelligence,artificial intelligence and the public sector—applications and challenges,"russo pa,duradoni m,guazzini a","wirtz bw,weyerer jc,geyer c","artificial intelligence,fairness,reputation,ultimatum game","ai applications,ai challenges,artificial intelligence,public sector",1
786,how self-perceived reputation affects fairness towards humans and artificial intelligence,the rising tide of artificial intelligence and business automation: developing an ethical framework,"russo pa,duradoni m,guazzini a","wright sa,schultz ae","artificial intelligence,fairness,reputation,ultimatum game","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",1
787,how self-perceived reputation affects fairness towards humans and artificial intelligence,achieving causal fairness through generative adversarial networks,"russo pa,duradoni m,guazzini a","xu d,wu y,yuan s,zhang l,wu x","artificial intelligence,fairness,reputation,ultimatum game","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
788,how self-perceived reputation affects fairness towards humans and artificial intelligence,algorithmic fairness in computational medicine,"russo pa,duradoni m,guazzini a","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","artificial intelligence,fairness,reputation,ultimatum game","algorithmic fairness,computational medicine",0
789,how self-perceived reputation affects fairness towards humans and artificial intelligence,effective exposure amortizing for fair top-k recommendation,"russo pa,duradoni m,guazzini a","yang t,xu z,ai q","artificial intelligence,fairness,reputation,ultimatum game","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
790,how self-perceived reputation affects fairness towards humans and artificial intelligence,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","russo pa,duradoni m,guazzini a","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","artificial intelligence,fairness,reputation,ultimatum game","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
791,how self-perceived reputation affects fairness towards humans and artificial intelligence,flexible and adaptive fairness-aware learning in non-stationary data streams,"russo pa,duradoni m,guazzini a","zhang w,zhang m,zhang j,liu z,...","artificial intelligence,fairness,reputation,ultimatum game","ai fairness,flexible fairness,online classification",0
792,how self-perceived reputation affects fairness towards humans and artificial intelligence,introduction to ai fairness,"russo pa,duradoni m,guazzini a","zhang y,bellamy r,liao qv,singh m","artificial intelligence,fairness,reputation,ultimatum game","algorithmic fairness,bias,decision support,discrimination-aware machine",0
793,how self-perceived reputation affects fairness towards humans and artificial intelligence,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","russo pa,duradoni m,guazzini a","zhou n,zhang z,nair vn,singhal h,chen j","artificial intelligence,fairness,reputation,ultimatum game","ai,ml,algorithm,bias,fairness",1
794,how self-perceived reputation affects fairness towards humans and artificial intelligence,applying differential privacy mechanism in artificial intelligence,"russo pa,duradoni m,guazzini a","zhu t,yu ps","artificial intelligence,fairness,reputation,ultimatum game","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
795,"on the relationship between explanations, fairness perceptions, and decisions",transparency and explainability of ai systems: ethical guidelines in practice,"schoeffer j,de-arteaga m,kuehl n","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
796,"on the relationship between explanations, fairness perceptions, and decisions","artificial intelligence, intersectionality, and the future of public health","schoeffer j,de-arteaga m,kuehl n","bauer gr,lizotte dj","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",1
797,"on the relationship between explanations, fairness perceptions, and decisions",using edge cases to disentangle fairness and solidarity in ai ethics,"schoeffer j,de-arteaga m,kuehl n",brusseau j,"author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","artificial intelligence,ethics,fairness,philosophy,solidarity",1
798,"on the relationship between explanations, fairness perceptions, and decisions",fairness & friends in the data science era,"schoeffer j,de-arteaga m,kuehl n","catania b,guerrini g,accinelli c","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
799,"on the relationship between explanations, fairness perceptions, and decisions",a sociotechnical view of algorithmic fairness,"schoeffer j,de-arteaga m,kuehl n","dolata m,feuerriegel s,schwabe g","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
800,"on the relationship between explanations, fairness perceptions, and decisions",ai-enabled radio resource allocation in 5g for urllc and embb users,"schoeffer j,de-arteaga m,kuehl n","elsayed m,erol-kantarci m","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
801,"on the relationship between explanations, fairness perceptions, and decisions","addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","schoeffer j,de-arteaga m,kuehl n","fletcher rr,nakeshimana a,...","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
802,"on the relationship between explanations, fairness perceptions, and decisions",rawls's original position and algorithmic fairness,"schoeffer j,de-arteaga m,kuehl n",franke u,"author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","algorithmic fairness,original position,veil of ignorance,veil of uncertainty",1
803,"on the relationship between explanations, fairness perceptions, and decisions",beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"schoeffer j,de-arteaga m,kuehl n","giovanola b,tiribelli s","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
804,"on the relationship between explanations, fairness perceptions, and decisions",speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"schoeffer j,de-arteaga m,kuehl n","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",0
805,"on the relationship between explanations, fairness perceptions, and decisions",who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"schoeffer j,de-arteaga m,kuehl n","helberger n,araujo t,de vreese ch","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","algorithmic fairness,automated-decision making,public perceptions",1
806,"on the relationship between explanations, fairness perceptions, and decisions",principal fairness for human and algorithmic decision-making,"schoeffer j,de-arteaga m,kuehl n","imai k,jiang z","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
807,"on the relationship between explanations, fairness perceptions, and decisions","introduction to the special issue on ai fairness, trust, and ethics","schoeffer j,de-arteaga m,kuehl n","jr lp,bansal g,melville n,...","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","ai bias,ai fairness,ai trust,artificial intelligence",0
808,"on the relationship between explanations, fairness perceptions, and decisions",exploring bias and fairness in artificial intelligence and machine learning algorithms,"schoeffer j,de-arteaga m,kuehl n","khakurel ub,abdelmoumin g,bajracharya a,rawat db","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",0
809,"on the relationship between explanations, fairness perceptions, and decisions",ai & intellectual property: towards an articulated public domain,"schoeffer j,de-arteaga m,kuehl n",kop m,"author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
810,"on the relationship between explanations, fairness perceptions, and decisions",a survey on datasets for fairness-aware machine learning,"schoeffer j,de-arteaga m,kuehl n","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
811,"on the relationship between explanations, fairness perceptions, and decisions",revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"schoeffer j,de-arteaga m,kuehl n","li c,xing w","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","fair ai,human-centered computing,math learning,perceived fairness",1
812,"on the relationship between explanations, fairness perceptions, and decisions",fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"schoeffer j,de-arteaga m,kuehl n","li j,chignell textperiodcenteredm","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
813,"on the relationship between explanations, fairness perceptions, and decisions",trustworthy ai: from principles to practices,"schoeffer j,de-arteaga m,kuehl n","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
814,"on the relationship between explanations, fairness perceptions, and decisions","assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","schoeffer j,de-arteaga m,kuehl n","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","ai,fairness,machine learning,software development practices",0
815,"on the relationship between explanations, fairness perceptions, and decisions","ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","schoeffer j,de-arteaga m,kuehl n","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
816,"on the relationship between explanations, fairness perceptions, and decisions",improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"schoeffer j,de-arteaga m,kuehl n","pessach d,shmueli e","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",0
817,"on the relationship between explanations, fairness perceptions, and decisions",linear versus nonlinear allocation rules in risk sharing under financial fairness,"schoeffer j,de-arteaga m,kuehl n",schumacher jm,"author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
818,"on the relationship between explanations, fairness perceptions, and decisions",artificial intelligence in human resources management: challenges and a path forward,"schoeffer j,de-arteaga m,kuehl n","tambe p,cappelli p,yakubovich v","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
819,"on the relationship between explanations, fairness perceptions, and decisions",a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"schoeffer j,de-arteaga m,kuehl n","timan t,grommé f","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","ai,fairness,framework,francisca grommé,ssrn,tjerk timan",1
820,"on the relationship between explanations, fairness perceptions, and decisions",artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"schoeffer j,de-arteaga m,kuehl n","undefined,ta l,cm c","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
821,"on the relationship between explanations, fairness perceptions, and decisions",ai ethics in industry: a research framework,"schoeffer j,de-arteaga m,kuehl n","ville v,kai-kristian k,pekka a","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
822,"on the relationship between explanations, fairness perceptions, and decisions",why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"schoeffer j,de-arteaga m,kuehl n","wachter s,mittelstadt b,russell c","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
823,"on the relationship between explanations, fairness perceptions, and decisions",user association and resource allocation in full-duplex relay aided noma systems,"schoeffer j,de-arteaga m,kuehl n","wang l,ai y,liu n,fei a","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
824,"on the relationship between explanations, fairness perceptions, and decisions",artificial intelligence and the public sector—applications and challenges,"schoeffer j,de-arteaga m,kuehl n","wirtz bw,weyerer jc,geyer c","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","ai applications,ai challenges,artificial intelligence,public sector",0
825,"on the relationship between explanations, fairness perceptions, and decisions",the rising tide of artificial intelligence and business automation: developing an ethical framework,"schoeffer j,de-arteaga m,kuehl n","wright sa,schultz ae","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",1
826,"on the relationship between explanations, fairness perceptions, and decisions",achieving causal fairness through generative adversarial networks,"schoeffer j,de-arteaga m,kuehl n","xu d,wu y,yuan s,zhang l,wu x","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
827,"on the relationship between explanations, fairness perceptions, and decisions",algorithmic fairness in computational medicine,"schoeffer j,de-arteaga m,kuehl n","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","algorithmic fairness,computational medicine",0
828,"on the relationship between explanations, fairness perceptions, and decisions",effective exposure amortizing for fair top-k recommendation,"schoeffer j,de-arteaga m,kuehl n","yang t,xu z,ai q","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
829,"on the relationship between explanations, fairness perceptions, and decisions","""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","schoeffer j,de-arteaga m,kuehl n","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
830,"on the relationship between explanations, fairness perceptions, and decisions",flexible and adaptive fairness-aware learning in non-stationary data streams,"schoeffer j,de-arteaga m,kuehl n","zhang w,zhang m,zhang j,liu z,...","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","ai fairness,flexible fairness,online classification",1
831,"on the relationship between explanations, fairness perceptions, and decisions",introduction to ai fairness,"schoeffer j,de-arteaga m,kuehl n","zhang y,bellamy r,liao qv,singh m","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","algorithmic fairness,bias,decision support,discrimination-aware machine",0
832,"on the relationship between explanations, fairness perceptions, and decisions","bias, fairness and accountability with artificial intelligence and machine learning algorithms","schoeffer j,de-arteaga m,kuehl n","zhou n,zhang z,nair vn,singhal h,chen j","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","ai,ml,algorithm,bias,fairness",1
833,"on the relationship between explanations, fairness perceptions, and decisions",applying differential privacy mechanism in artificial intelligence,"schoeffer j,de-arteaga m,kuehl n","zhu t,yu ps","author keywords algorithmic decision-making,explanations,fairness,human-ai complementarity,perceptions,reliance","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
834,linear versus nonlinear allocation rules in risk sharing under financial fairness,transparency and explainability of ai systems: ethical guidelines in practice,schumacher jm,"balasubramaniam n,kauppinen m,hiekkanen k,kujala s","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
835,linear versus nonlinear allocation rules in risk sharing under financial fairness,"artificial intelligence, intersectionality, and the future of public health",schumacher jm,"bauer gr,lizotte dj","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
836,linear versus nonlinear allocation rules in risk sharing under financial fairness,fairness & friends in the data science era,schumacher jm,"catania b,guerrini g,accinelli c","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
837,linear versus nonlinear allocation rules in risk sharing under financial fairness,a sociotechnical view of algorithmic fairness,schumacher jm,"dolata m,feuerriegel s,schwabe g","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
838,linear versus nonlinear allocation rules in risk sharing under financial fairness,ai-enabled radio resource allocation in 5g for urllc and embb users,schumacher jm,"elsayed m,erol-kantarci m","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
839,linear versus nonlinear allocation rules in risk sharing under financial fairness,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health",schumacher jm,"fletcher rr,nakeshimana a,...","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",1
840,linear versus nonlinear allocation rules in risk sharing under financial fairness,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,schumacher jm,"giovanola b,tiribelli s","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",1
841,linear versus nonlinear allocation rules in risk sharing under financial fairness,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,schumacher jm,"helberger n,araujo t,de vreese ch","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","algorithmic fairness,automated-decision making,public perceptions",0
842,linear versus nonlinear allocation rules in risk sharing under financial fairness,principal fairness for human and algorithmic decision-making,schumacher jm,"imai k,jiang z","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
843,linear versus nonlinear allocation rules in risk sharing under financial fairness,"introduction to the special issue on ai fairness, trust, and ethics",schumacher jm,"jr lp,bansal g,melville n,...","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","ai bias,ai fairness,ai trust,artificial intelligence",0
844,linear versus nonlinear allocation rules in risk sharing under financial fairness,exploring bias and fairness in artificial intelligence and machine learning algorithms,schumacher jm,"khakurel ub,abdelmoumin g,bajracharya a,rawat db","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",0
845,linear versus nonlinear allocation rules in risk sharing under financial fairness,ai & intellectual property: towards an articulated public domain,schumacher jm,kop m,"borch theorem,risk sharing,allocation rules,financial fairness,syndicates","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
846,linear versus nonlinear allocation rules in risk sharing under financial fairness,a survey on datasets for fairness-aware machine learning,schumacher jm,"le quy t,roy a,iosifidis v,zhang w,ntoutsi e","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
847,linear versus nonlinear allocation rules in risk sharing under financial fairness,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,schumacher jm,"li c,xing w","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","fair ai,human-centered computing,math learning,perceived fairness",0
848,linear versus nonlinear allocation rules in risk sharing under financial fairness,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,schumacher jm,"li j,chignell textperiodcenteredm","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
849,linear versus nonlinear allocation rules in risk sharing under financial fairness,trustworthy ai: from principles to practices,schumacher jm,"libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
850,linear versus nonlinear allocation rules in risk sharing under financial fairness,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support",schumacher jm,"madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","ai,fairness,machine learning,software development practices",0
851,linear versus nonlinear allocation rules in risk sharing under financial fairness,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox",schumacher jm,"pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
852,linear versus nonlinear allocation rules in risk sharing under financial fairness,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,schumacher jm,"pessach d,shmueli e","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",1
853,linear versus nonlinear allocation rules in risk sharing under financial fairness,artificial intelligence in human resources management: challenges and a path forward,schumacher jm,"tambe p,cappelli p,yakubovich v","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
854,linear versus nonlinear allocation rules in risk sharing under financial fairness,ai ethics in industry: a research framework,schumacher jm,"ville v,kai-kristian k,pekka a","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
855,linear versus nonlinear allocation rules in risk sharing under financial fairness,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,schumacher jm,"wachter s,mittelstadt b,russell c","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
856,linear versus nonlinear allocation rules in risk sharing under financial fairness,user association and resource allocation in full-duplex relay aided noma systems,schumacher jm,"wang l,ai y,liu n,fei a","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
857,linear versus nonlinear allocation rules in risk sharing under financial fairness,artificial intelligence and the public sector—applications and challenges,schumacher jm,"wirtz bw,weyerer jc,geyer c","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","ai applications,ai challenges,artificial intelligence,public sector",0
858,linear versus nonlinear allocation rules in risk sharing under financial fairness,the rising tide of artificial intelligence and business automation: developing an ethical framework,schumacher jm,"wright sa,schultz ae","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",0
859,linear versus nonlinear allocation rules in risk sharing under financial fairness,achieving causal fairness through generative adversarial networks,schumacher jm,"xu d,wu y,yuan s,zhang l,wu x","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
860,linear versus nonlinear allocation rules in risk sharing under financial fairness,algorithmic fairness in computational medicine,schumacher jm,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","algorithmic fairness,computational medicine",0
861,linear versus nonlinear allocation rules in risk sharing under financial fairness,effective exposure amortizing for fair top-k recommendation,schumacher jm,"yang t,xu z,ai q","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
862,linear versus nonlinear allocation rules in risk sharing under financial fairness,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",schumacher jm,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
863,linear versus nonlinear allocation rules in risk sharing under financial fairness,flexible and adaptive fairness-aware learning in non-stationary data streams,schumacher jm,"zhang w,zhang m,zhang j,liu z,...","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","ai fairness,flexible fairness,online classification",0
864,linear versus nonlinear allocation rules in risk sharing under financial fairness,"bias, fairness and accountability with artificial intelligence and machine learning algorithms",schumacher jm,"zhou n,zhang z,nair vn,singhal h,chen j","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","ai,ml,algorithm,bias,fairness",1
865,linear versus nonlinear allocation rules in risk sharing under financial fairness,applying differential privacy mechanism in artificial intelligence,schumacher jm,"zhu t,yu ps","borch theorem,risk sharing,allocation rules,financial fairness,syndicates","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
866,artificial intelligence in human resources management: challenges and a path forward,"artificial intelligence, intersectionality, and the future of public health","tambe p,cappelli p,yakubovich v","bauer gr,lizotte dj","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",1
867,artificial intelligence in human resources management: challenges and a path forward,a sociotechnical view of algorithmic fairness,"tambe p,cappelli p,yakubovich v","dolata m,feuerriegel s,schwabe g","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
868,artificial intelligence in human resources management: challenges and a path forward,ai-enabled radio resource allocation in 5g for urllc and embb users,"tambe p,cappelli p,yakubovich v","elsayed m,erol-kantarci m","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
869,artificial intelligence in human resources management: challenges and a path forward,principal fairness for human and algorithmic decision-making,"tambe p,cappelli p,yakubovich v","imai k,jiang z","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
870,artificial intelligence in human resources management: challenges and a path forward,a survey on datasets for fairness-aware machine learning,"tambe p,cappelli p,yakubovich v","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
871,artificial intelligence in human resources management: challenges and a path forward,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"tambe p,cappelli p,yakubovich v","li c,xing w","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems","fair ai,human-centered computing,math learning,perceived fairness",0
872,artificial intelligence in human resources management: challenges and a path forward,trustworthy ai: from principles to practices,"tambe p,cappelli p,yakubovich v","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
873,artificial intelligence in human resources management: challenges and a path forward,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","tambe p,cappelli p,yakubovich v","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
874,artificial intelligence in human resources management: challenges and a path forward,flexible and adaptive fairness-aware learning in non-stationary data streams,"tambe p,cappelli p,yakubovich v","zhang w,zhang m,zhang j,liu z,...","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems","ai fairness,flexible fairness,online classification",0
875,artificial intelligence in human resources management: challenges and a path forward,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","tambe p,cappelli p,yakubovich v","zhou n,zhang z,nair vn,singhal h,chen j","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems","ai,ml,algorithm,bias,fairness",0
876,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,transparency and explainability of ai systems: ethical guidelines in practice,"timan t,grommé f","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
877,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"artificial intelligence, intersectionality, and the future of public health","timan t,grommé f","bauer gr,lizotte dj","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
878,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,fairness & friends in the data science era,"timan t,grommé f","catania b,guerrini g,accinelli c","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
879,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,a sociotechnical view of algorithmic fairness,"timan t,grommé f","dolata m,feuerriegel s,schwabe g","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
880,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,ai-enabled radio resource allocation in 5g for urllc and embb users,"timan t,grommé f","elsayed m,erol-kantarci m","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
881,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","timan t,grommé f","fletcher rr,nakeshimana a,...","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
882,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,rawls's original position and algorithmic fairness,"timan t,grommé f",franke u,"ai,fairness,framework,francisca grommé,ssrn,tjerk timan","algorithmic fairness,original position,veil of ignorance,veil of uncertainty",0
883,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"timan t,grommé f","giovanola b,tiribelli s","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
884,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,speciesist bias in ai: how ai applications perpetuate discrimination and unfair outcomes against animals,"timan t,grommé f","hagendorff t,bossert ln,yip textperiodcentered,tse f,singer textperiodcenteredp","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","artificial intelligence,ethics,fairness,machine learning,speciesism,speciesist bias",0
885,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"timan t,grommé f","helberger n,araujo t,de vreese ch","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","algorithmic fairness,automated-decision making,public perceptions",1
886,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,principal fairness for human and algorithmic decision-making,"timan t,grommé f","imai k,jiang z","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
887,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"introduction to the special issue on ai fairness, trust, and ethics","timan t,grommé f","jr lp,bansal g,melville n,...","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","ai bias,ai fairness,ai trust,artificial intelligence",1
888,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,exploring bias and fairness in artificial intelligence and machine learning algorithms,"timan t,grommé f","khakurel ub,abdelmoumin g,bajracharya a,rawat db","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
889,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,ai & intellectual property: towards an articulated public domain,"timan t,grommé f",kop m,"ai,fairness,framework,francisca grommé,ssrn,tjerk timan","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
890,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,a survey on datasets for fairness-aware machine learning,"timan t,grommé f","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
891,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"timan t,grommé f","li c,xing w","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","fair ai,human-centered computing,math learning,perceived fairness",1
892,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"timan t,grommé f","li j,chignell textperiodcenteredm","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
893,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,trustworthy ai: from principles to practices,"timan t,grommé f","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
894,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","timan t,grommé f","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","ai,fairness,machine learning,software development practices",1
895,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","timan t,grommé f","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
896,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"timan t,grommé f","pessach d,shmueli e","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",1
897,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,linear versus nonlinear allocation rules in risk sharing under financial fairness,"timan t,grommé f",schumacher jm,"ai,fairness,framework,francisca grommé,ssrn,tjerk timan","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
898,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,artificial intelligence in human resources management: challenges and a path forward,"timan t,grommé f","tambe p,cappelli p,yakubovich v","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
899,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"timan t,grommé f","undefined,ta l,cm c","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining",0
900,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,ai ethics in industry: a research framework,"timan t,grommé f","ville v,kai-kristian k,pekka a","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
901,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"timan t,grommé f","wachter s,mittelstadt b,russell c","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
902,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,user association and resource allocation in full-duplex relay aided noma systems,"timan t,grommé f","wang l,ai y,liu n,fei a","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
903,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,artificial intelligence and the public sector—applications and challenges,"timan t,grommé f","wirtz bw,weyerer jc,geyer c","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","ai applications,ai challenges,artificial intelligence,public sector",0
904,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,the rising tide of artificial intelligence and business automation: developing an ethical framework,"timan t,grommé f","wright sa,schultz ae","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",1
905,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,achieving causal fairness through generative adversarial networks,"timan t,grommé f","xu d,wu y,yuan s,zhang l,wu x","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
906,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,algorithmic fairness in computational medicine,"timan t,grommé f","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","algorithmic fairness,computational medicine",1
907,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,effective exposure amortizing for fair top-k recommendation,"timan t,grommé f","yang t,xu z,ai q","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
908,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","timan t,grommé f","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
909,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,flexible and adaptive fairness-aware learning in non-stationary data streams,"timan t,grommé f","zhang w,zhang m,zhang j,liu z,...","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","ai fairness,flexible fairness,online classification",1
910,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,introduction to ai fairness,"timan t,grommé f","zhang y,bellamy r,liao qv,singh m","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","algorithmic fairness,bias,decision support,discrimination-aware machine",0
911,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","timan t,grommé f","zhou n,zhang z,nair vn,singhal h,chen j","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","ai,ml,algorithm,bias,fairness",1
912,a framework for social fairness – insights from two algorithmic decision – making controversies in the netherlands,applying differential privacy mechanism in artificial intelligence,"timan t,grommé f","zhu t,yu ps","ai,fairness,framework,francisca grommé,ssrn,tjerk timan","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
913,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,transparency and explainability of ai systems: ethical guidelines in practice,"undefined,ta l,cm c","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
914,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"artificial intelligence, intersectionality, and the future of public health","undefined,ta l,cm c","bauer gr,lizotte dj","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
915,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,fairness & friends in the data science era,"undefined,ta l,cm c","catania b,guerrini g,accinelli c","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
916,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,a sociotechnical view of algorithmic fairness,"undefined,ta l,cm c","dolata m,feuerriegel s,schwabe g","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
917,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,ai-enabled radio resource allocation in 5g for urllc and embb users,"undefined,ta l,cm c","elsayed m,erol-kantarci m","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
918,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","undefined,ta l,cm c","fletcher rr,nakeshimana a,...","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",1
919,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"undefined,ta l,cm c","giovanola b,tiribelli s","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
920,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"undefined,ta l,cm c","helberger n,araujo t,de vreese ch","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","algorithmic fairness,automated-decision making,public perceptions",0
921,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,principal fairness for human and algorithmic decision-making,"undefined,ta l,cm c","imai k,jiang z","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
922,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"introduction to the special issue on ai fairness, trust, and ethics","undefined,ta l,cm c","jr lp,bansal g,melville n,...","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","ai bias,ai fairness,ai trust,artificial intelligence",0
923,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,exploring bias and fairness in artificial intelligence and machine learning algorithms,"undefined,ta l,cm c","khakurel ub,abdelmoumin g,bajracharya a,rawat db","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",0
924,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,ai & intellectual property: towards an articulated public domain,"undefined,ta l,cm c",kop m,"europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
925,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,a survey on datasets for fairness-aware machine learning,"undefined,ta l,cm c","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
926,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"undefined,ta l,cm c","li c,xing w","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","fair ai,human-centered computing,math learning,perceived fairness",0
927,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"undefined,ta l,cm c","li j,chignell textperiodcenteredm","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
928,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,trustworthy ai: from principles to practices,"undefined,ta l,cm c","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
929,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","undefined,ta l,cm c","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","ai,fairness,machine learning,software development practices",1
930,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","undefined,ta l,cm c","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
931,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"undefined,ta l,cm c","pessach d,shmueli e","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",0
932,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,linear versus nonlinear allocation rules in risk sharing under financial fairness,"undefined,ta l,cm c",schumacher jm,"europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
933,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,artificial intelligence in human resources management: challenges and a path forward,"undefined,ta l,cm c","tambe p,cappelli p,yakubovich v","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
934,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,ai ethics in industry: a research framework,"undefined,ta l,cm c","ville v,kai-kristian k,pekka a","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
935,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"undefined,ta l,cm c","wachter s,mittelstadt b,russell c","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
936,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,user association and resource allocation in full-duplex relay aided noma systems,"undefined,ta l,cm c","wang l,ai y,liu n,fei a","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
937,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,artificial intelligence and the public sector—applications and challenges,"undefined,ta l,cm c","wirtz bw,weyerer jc,geyer c","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","ai applications,ai challenges,artificial intelligence,public sector",0
938,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,the rising tide of artificial intelligence and business automation: developing an ethical framework,"undefined,ta l,cm c","wright sa,schultz ae","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",0
939,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,achieving causal fairness through generative adversarial networks,"undefined,ta l,cm c","xu d,wu y,yuan s,zhang l,wu x","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
940,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,algorithmic fairness in computational medicine,"undefined,ta l,cm c","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","algorithmic fairness,computational medicine",0
941,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,effective exposure amortizing for fair top-k recommendation,"undefined,ta l,cm c","yang t,xu z,ai q","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
942,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","undefined,ta l,cm c","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
943,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,flexible and adaptive fairness-aware learning in non-stationary data streams,"undefined,ta l,cm c","zhang w,zhang m,zhang j,liu z,...","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","ai fairness,flexible fairness,online classification",0
944,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,introduction to ai fairness,"undefined,ta l,cm c","zhang y,bellamy r,liao qv,singh m","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","algorithmic fairness,bias,decision support,discrimination-aware machine",0
945,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","undefined,ta l,cm c","zhou n,zhang z,nair vn,singhal h,chen j","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","ai,ml,algorithm,bias,fairness",0
946,artificial intelligence applications for older adults and people with disabilities: balancing safety and autonomy: proceedings of a workshop—in brief,applying differential privacy mechanism in artificial intelligence,"undefined,ta l,cm c","zhu t,yu ps","europe pmc,europe pubmed central,orcids,rest apis,abstracts,bioinformatics,biological patents,biomedical journals,biomedical research,citation networks,citation search,clinical guidelines,full text,journal articles,life sciences,literature search,open access,research articles,text mining","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
947,ai ethics in industry: a research framework,transparency and explainability of ai systems: ethical guidelines in practice,"ville v,kai-kristian k,pekka a","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
948,ai ethics in industry: a research framework,"artificial intelligence, intersectionality, and the future of public health","ville v,kai-kristian k,pekka a","bauer gr,lizotte dj","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
949,ai ethics in industry: a research framework,fairness & friends in the data science era,"ville v,kai-kristian k,pekka a","catania b,guerrini g,accinelli c","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
950,ai ethics in industry: a research framework,a sociotechnical view of algorithmic fairness,"ville v,kai-kristian k,pekka a","dolata m,feuerriegel s,schwabe g","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
951,ai ethics in industry: a research framework,ai-enabled radio resource allocation in 5g for urllc and embb users,"ville v,kai-kristian k,pekka a","elsayed m,erol-kantarci m","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
952,ai ethics in industry: a research framework,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"ville v,kai-kristian k,pekka a","giovanola b,tiribelli s","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
953,ai ethics in industry: a research framework,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"ville v,kai-kristian k,pekka a","helberger n,araujo t,de vreese ch","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","algorithmic fairness,automated-decision making,public perceptions",0
954,ai ethics in industry: a research framework,principal fairness for human and algorithmic decision-making,"ville v,kai-kristian k,pekka a","imai k,jiang z","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
955,ai ethics in industry: a research framework,"introduction to the special issue on ai fairness, trust, and ethics","ville v,kai-kristian k,pekka a","jr lp,bansal g,melville n,...","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","ai bias,ai fairness,ai trust,artificial intelligence",1
956,ai ethics in industry: a research framework,exploring bias and fairness in artificial intelligence and machine learning algorithms,"ville v,kai-kristian k,pekka a","khakurel ub,abdelmoumin g,bajracharya a,rawat db","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",0
957,ai ethics in industry: a research framework,ai & intellectual property: towards an articulated public domain,"ville v,kai-kristian k,pekka a",kop m,"ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
958,ai ethics in industry: a research framework,a survey on datasets for fairness-aware machine learning,"ville v,kai-kristian k,pekka a","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
959,ai ethics in industry: a research framework,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"ville v,kai-kristian k,pekka a","li c,xing w","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","fair ai,human-centered computing,math learning,perceived fairness",0
960,ai ethics in industry: a research framework,trustworthy ai: from principles to practices,"ville v,kai-kristian k,pekka a","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
961,ai ethics in industry: a research framework,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","ville v,kai-kristian k,pekka a","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","ai,fairness,machine learning,software development practices",0
962,ai ethics in industry: a research framework,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","ville v,kai-kristian k,pekka a","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
963,ai ethics in industry: a research framework,artificial intelligence in human resources management: challenges and a path forward,"ville v,kai-kristian k,pekka a","tambe p,cappelli p,yakubovich v","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
964,ai ethics in industry: a research framework,artificial intelligence and the public sector—applications and challenges,"ville v,kai-kristian k,pekka a","wirtz bw,weyerer jc,geyer c","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","ai applications,ai challenges,artificial intelligence,public sector",0
965,ai ethics in industry: a research framework,effective exposure amortizing for fair top-k recommendation,"ville v,kai-kristian k,pekka a","yang t,xu z,ai q","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
966,ai ethics in industry: a research framework,flexible and adaptive fairness-aware learning in non-stationary data streams,"ville v,kai-kristian k,pekka a","zhang w,zhang m,zhang j,liu z,...","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","ai fairness,flexible fairness,online classification",0
967,ai ethics in industry: a research framework,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","ville v,kai-kristian k,pekka a","zhou n,zhang z,nair vn,singhal h,chen j","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency","ai,ml,algorithm,bias,fairness",0
968,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,transparency and explainability of ai systems: ethical guidelines in practice,"wachter s,mittelstadt b,russell c","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
969,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"artificial intelligence, intersectionality, and the future of public health","wachter s,mittelstadt b,russell c","bauer gr,lizotte dj","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
970,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,fairness & friends in the data science era,"wachter s,mittelstadt b,russell c","catania b,guerrini g,accinelli c","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
971,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,a sociotechnical view of algorithmic fairness,"wachter s,mittelstadt b,russell c","dolata m,feuerriegel s,schwabe g","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
972,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,ai-enabled radio resource allocation in 5g for urllc and embb users,"wachter s,mittelstadt b,russell c","elsayed m,erol-kantarci m","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
973,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","wachter s,mittelstadt b,russell c","fletcher rr,nakeshimana a,...","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
974,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"wachter s,mittelstadt b,russell c","giovanola b,tiribelli s","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
975,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"wachter s,mittelstadt b,russell c","helberger n,araujo t,de vreese ch","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","algorithmic fairness,automated-decision making,public perceptions",1
976,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,principal fairness for human and algorithmic decision-making,"wachter s,mittelstadt b,russell c","imai k,jiang z","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
977,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"introduction to the special issue on ai fairness, trust, and ethics","wachter s,mittelstadt b,russell c","jr lp,bansal g,melville n,...","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","ai bias,ai fairness,ai trust,artificial intelligence",1
978,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,exploring bias and fairness in artificial intelligence and machine learning algorithms,"wachter s,mittelstadt b,russell c","khakurel ub,abdelmoumin g,bajracharya a,rawat db","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
979,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,ai & intellectual property: towards an articulated public domain,"wachter s,mittelstadt b,russell c",kop m,"algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
980,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,a survey on datasets for fairness-aware machine learning,"wachter s,mittelstadt b,russell c","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
981,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"wachter s,mittelstadt b,russell c","li c,xing w","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","fair ai,human-centered computing,math learning,perceived fairness",0
982,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,trustworthy ai: from principles to practices,"wachter s,mittelstadt b,russell c","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
983,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","wachter s,mittelstadt b,russell c","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","ai,fairness,machine learning,software development practices",1
984,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","wachter s,mittelstadt b,russell c","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
985,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"wachter s,mittelstadt b,russell c","pessach d,shmueli e","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",0
986,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,artificial intelligence in human resources management: challenges and a path forward,"wachter s,mittelstadt b,russell c","tambe p,cappelli p,yakubovich v","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
987,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,ai ethics in industry: a research framework,"wachter s,mittelstadt b,russell c","ville v,kai-kristian k,pekka a","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
988,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,artificial intelligence and the public sector—applications and challenges,"wachter s,mittelstadt b,russell c","wirtz bw,weyerer jc,geyer c","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","ai applications,ai challenges,artificial intelligence,public sector",0
989,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,algorithmic fairness in computational medicine,"wachter s,mittelstadt b,russell c","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","algorithmic fairness,computational medicine",0
990,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,effective exposure amortizing for fair top-k recommendation,"wachter s,mittelstadt b,russell c","yang t,xu z,ai q","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
991,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,flexible and adaptive fairness-aware learning in non-stationary data streams,"wachter s,mittelstadt b,russell c","zhang w,zhang m,zhang j,liu z,...","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","ai fairness,flexible fairness,online classification",1
992,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","wachter s,mittelstadt b,russell c","zhou n,zhang z,nair vn,singhal h,chen j","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","ai,ml,algorithm,bias,fairness",1
993,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,applying differential privacy mechanism in artificial intelligence,"wachter s,mittelstadt b,russell c","zhu t,yu ps","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
994,user association and resource allocation in full-duplex relay aided noma systems,transparency and explainability of ai systems: ethical guidelines in practice,"wang l,ai y,liu n,fei a","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
995,user association and resource allocation in full-duplex relay aided noma systems,"artificial intelligence, intersectionality, and the future of public health","wang l,ai y,liu n,fei a","bauer gr,lizotte dj","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
996,user association and resource allocation in full-duplex relay aided noma systems,fairness & friends in the data science era,"wang l,ai y,liu n,fei a","catania b,guerrini g,accinelli c","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
997,user association and resource allocation in full-duplex relay aided noma systems,a sociotechnical view of algorithmic fairness,"wang l,ai y,liu n,fei a","dolata m,feuerriegel s,schwabe g","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
998,user association and resource allocation in full-duplex relay aided noma systems,ai-enabled radio resource allocation in 5g for urllc and embb users,"wang l,ai y,liu n,fei a","elsayed m,erol-kantarci m","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
999,user association and resource allocation in full-duplex relay aided noma systems,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","wang l,ai y,liu n,fei a","fletcher rr,nakeshimana a,...","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
1000,user association and resource allocation in full-duplex relay aided noma systems,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"wang l,ai y,liu n,fei a","giovanola b,tiribelli s","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
1001,user association and resource allocation in full-duplex relay aided noma systems,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"wang l,ai y,liu n,fei a","helberger n,araujo t,de vreese ch","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","algorithmic fairness,automated-decision making,public perceptions",0
1002,user association and resource allocation in full-duplex relay aided noma systems,principal fairness for human and algorithmic decision-making,"wang l,ai y,liu n,fei a","imai k,jiang z","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
1003,user association and resource allocation in full-duplex relay aided noma systems,"introduction to the special issue on ai fairness, trust, and ethics","wang l,ai y,liu n,fei a","jr lp,bansal g,melville n,...","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","ai bias,ai fairness,ai trust,artificial intelligence",0
1004,user association and resource allocation in full-duplex relay aided noma systems,exploring bias and fairness in artificial intelligence and machine learning algorithms,"wang l,ai y,liu n,fei a","khakurel ub,abdelmoumin g,bajracharya a,rawat db","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",0
1005,user association and resource allocation in full-duplex relay aided noma systems,ai & intellectual property: towards an articulated public domain,"wang l,ai y,liu n,fei a",kop m,"full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
1006,user association and resource allocation in full-duplex relay aided noma systems,a survey on datasets for fairness-aware machine learning,"wang l,ai y,liu n,fei a","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
1007,user association and resource allocation in full-duplex relay aided noma systems,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"wang l,ai y,liu n,fei a","li c,xing w","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","fair ai,human-centered computing,math learning,perceived fairness",0
1008,user association and resource allocation in full-duplex relay aided noma systems,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"wang l,ai y,liu n,fei a","li j,chignell textperiodcenteredm","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
1009,user association and resource allocation in full-duplex relay aided noma systems,trustworthy ai: from principles to practices,"wang l,ai y,liu n,fei a","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
1010,user association and resource allocation in full-duplex relay aided noma systems,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","wang l,ai y,liu n,fei a","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","ai,fairness,machine learning,software development practices",0
1011,user association and resource allocation in full-duplex relay aided noma systems,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","wang l,ai y,liu n,fei a","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
1012,user association and resource allocation in full-duplex relay aided noma systems,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"wang l,ai y,liu n,fei a","pessach d,shmueli e","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",0
1013,user association and resource allocation in full-duplex relay aided noma systems,artificial intelligence in human resources management: challenges and a path forward,"wang l,ai y,liu n,fei a","tambe p,cappelli p,yakubovich v","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
1014,user association and resource allocation in full-duplex relay aided noma systems,ai ethics in industry: a research framework,"wang l,ai y,liu n,fei a","ville v,kai-kristian k,pekka a","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
1015,user association and resource allocation in full-duplex relay aided noma systems,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"wang l,ai y,liu n,fei a","wachter s,mittelstadt b,russell c","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
1016,user association and resource allocation in full-duplex relay aided noma systems,artificial intelligence and the public sector—applications and challenges,"wang l,ai y,liu n,fei a","wirtz bw,weyerer jc,geyer c","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","ai applications,ai challenges,artificial intelligence,public sector",0
1017,user association and resource allocation in full-duplex relay aided noma systems,algorithmic fairness in computational medicine,"wang l,ai y,liu n,fei a","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","algorithmic fairness,computational medicine",0
1018,user association and resource allocation in full-duplex relay aided noma systems,effective exposure amortizing for fair top-k recommendation,"wang l,ai y,liu n,fei a","yang t,xu z,ai q","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
1019,user association and resource allocation in full-duplex relay aided noma systems,flexible and adaptive fairness-aware learning in non-stationary data streams,"wang l,ai y,liu n,fei a","zhang w,zhang m,zhang j,liu z,...","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","ai fairness,flexible fairness,online classification",0
1020,user association and resource allocation in full-duplex relay aided noma systems,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","wang l,ai y,liu n,fei a","zhou n,zhang z,nair vn,singhal h,chen j","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","ai,ml,algorithm,bias,fairness",0
1021,user association and resource allocation in full-duplex relay aided noma systems,applying differential privacy mechanism in artificial intelligence,"wang l,ai y,liu n,fei a","zhu t,yu ps","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
1022,artificial intelligence and the public sector—applications and challenges,"artificial intelligence, intersectionality, and the future of public health","wirtz bw,weyerer jc,geyer c","bauer gr,lizotte dj","ai applications,ai challenges,artificial intelligence,public sector","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",1
1023,artificial intelligence and the public sector—applications and challenges,a sociotechnical view of algorithmic fairness,"wirtz bw,weyerer jc,geyer c","dolata m,feuerriegel s,schwabe g","ai applications,ai challenges,artificial intelligence,public sector","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
1024,artificial intelligence and the public sector—applications and challenges,ai-enabled radio resource allocation in 5g for urllc and embb users,"wirtz bw,weyerer jc,geyer c","elsayed m,erol-kantarci m","ai applications,ai challenges,artificial intelligence,public sector","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
1025,artificial intelligence and the public sector—applications and challenges,principal fairness for human and algorithmic decision-making,"wirtz bw,weyerer jc,geyer c","imai k,jiang z","ai applications,ai challenges,artificial intelligence,public sector","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
1026,artificial intelligence and the public sector—applications and challenges,a survey on datasets for fairness-aware machine learning,"wirtz bw,weyerer jc,geyer c","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","ai applications,ai challenges,artificial intelligence,public sector","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
1027,artificial intelligence and the public sector—applications and challenges,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"wirtz bw,weyerer jc,geyer c","li c,xing w","ai applications,ai challenges,artificial intelligence,public sector","fair ai,human-centered computing,math learning,perceived fairness",0
1028,artificial intelligence and the public sector—applications and challenges,trustworthy ai: from principles to practices,"wirtz bw,weyerer jc,geyer c","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","ai applications,ai challenges,artificial intelligence,public sector","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
1029,artificial intelligence and the public sector—applications and challenges,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","wirtz bw,weyerer jc,geyer c","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","ai applications,ai challenges,artificial intelligence,public sector","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
1030,artificial intelligence and the public sector—applications and challenges,artificial intelligence in human resources management: challenges and a path forward,"wirtz bw,weyerer jc,geyer c","tambe p,cappelli p,yakubovich v","ai applications,ai challenges,artificial intelligence,public sector","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",1
1031,artificial intelligence and the public sector—applications and challenges,flexible and adaptive fairness-aware learning in non-stationary data streams,"wirtz bw,weyerer jc,geyer c","zhang w,zhang m,zhang j,liu z,...","ai applications,ai challenges,artificial intelligence,public sector","ai fairness,flexible fairness,online classification",1
1032,artificial intelligence and the public sector—applications and challenges,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","wirtz bw,weyerer jc,geyer c","zhou n,zhang z,nair vn,singhal h,chen j","ai applications,ai challenges,artificial intelligence,public sector","ai,ml,algorithm,bias,fairness",1
1033,the rising tide of artificial intelligence and business automation: developing an ethical framework,transparency and explainability of ai systems: ethical guidelines in practice,"wright sa,schultz ae","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","ai systems,ethical guidelines,explainability,quality requirements,transparency",1
1034,the rising tide of artificial intelligence and business automation: developing an ethical framework,"artificial intelligence, intersectionality, and the future of public health","wright sa,schultz ae","bauer gr,lizotte dj","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",1
1035,the rising tide of artificial intelligence and business automation: developing an ethical framework,fairness & friends in the data science era,"wright sa,schultz ae","catania b,guerrini g,accinelli c","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
1036,the rising tide of artificial intelligence and business automation: developing an ethical framework,a sociotechnical view of algorithmic fairness,"wright sa,schultz ae","dolata m,feuerriegel s,schwabe g","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
1037,the rising tide of artificial intelligence and business automation: developing an ethical framework,ai-enabled radio resource allocation in 5g for urllc and embb users,"wright sa,schultz ae","elsayed m,erol-kantarci m","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
1038,the rising tide of artificial intelligence and business automation: developing an ethical framework,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","wright sa,schultz ae","fletcher rr,nakeshimana a,...","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",1
1039,the rising tide of artificial intelligence and business automation: developing an ethical framework,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"wright sa,schultz ae","giovanola b,tiribelli s","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
1040,the rising tide of artificial intelligence and business automation: developing an ethical framework,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"wright sa,schultz ae","helberger n,araujo t,de vreese ch","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","algorithmic fairness,automated-decision making,public perceptions",0
1041,the rising tide of artificial intelligence and business automation: developing an ethical framework,principal fairness for human and algorithmic decision-making,"wright sa,schultz ae","imai k,jiang z","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
1042,the rising tide of artificial intelligence and business automation: developing an ethical framework,"introduction to the special issue on ai fairness, trust, and ethics","wright sa,schultz ae","jr lp,bansal g,melville n,...","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","ai bias,ai fairness,ai trust,artificial intelligence",1
1043,the rising tide of artificial intelligence and business automation: developing an ethical framework,exploring bias and fairness in artificial intelligence and machine learning algorithms,"wright sa,schultz ae","khakurel ub,abdelmoumin g,bajracharya a,rawat db","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
1044,the rising tide of artificial intelligence and business automation: developing an ethical framework,ai & intellectual property: towards an articulated public domain,"wright sa,schultz ae",kop m,"artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
1045,the rising tide of artificial intelligence and business automation: developing an ethical framework,a survey on datasets for fairness-aware machine learning,"wright sa,schultz ae","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
1046,the rising tide of artificial intelligence and business automation: developing an ethical framework,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"wright sa,schultz ae","li c,xing w","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","fair ai,human-centered computing,math learning,perceived fairness",0
1047,the rising tide of artificial intelligence and business automation: developing an ethical framework,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"wright sa,schultz ae","li j,chignell textperiodcenteredm","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
1048,the rising tide of artificial intelligence and business automation: developing an ethical framework,trustworthy ai: from principles to practices,"wright sa,schultz ae","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
1049,the rising tide of artificial intelligence and business automation: developing an ethical framework,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","wright sa,schultz ae","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","ai,fairness,machine learning,software development practices",0
1050,the rising tide of artificial intelligence and business automation: developing an ethical framework,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","wright sa,schultz ae","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
1051,the rising tide of artificial intelligence and business automation: developing an ethical framework,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"wright sa,schultz ae","pessach d,shmueli e","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",1
1052,the rising tide of artificial intelligence and business automation: developing an ethical framework,artificial intelligence in human resources management: challenges and a path forward,"wright sa,schultz ae","tambe p,cappelli p,yakubovich v","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
1053,the rising tide of artificial intelligence and business automation: developing an ethical framework,ai ethics in industry: a research framework,"wright sa,schultz ae","ville v,kai-kristian k,pekka a","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
1054,the rising tide of artificial intelligence and business automation: developing an ethical framework,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"wright sa,schultz ae","wachter s,mittelstadt b,russell c","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",1
1055,the rising tide of artificial intelligence and business automation: developing an ethical framework,user association and resource allocation in full-duplex relay aided noma systems,"wright sa,schultz ae","wang l,ai y,liu n,fei a","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
1056,the rising tide of artificial intelligence and business automation: developing an ethical framework,artificial intelligence and the public sector—applications and challenges,"wright sa,schultz ae","wirtz bw,weyerer jc,geyer c","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","ai applications,ai challenges,artificial intelligence,public sector",0
1057,the rising tide of artificial intelligence and business automation: developing an ethical framework,achieving causal fairness through generative adversarial networks,"wright sa,schultz ae","xu d,wu y,yuan s,zhang l,wu x","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",1
1058,the rising tide of artificial intelligence and business automation: developing an ethical framework,algorithmic fairness in computational medicine,"wright sa,schultz ae","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","algorithmic fairness,computational medicine",0
1059,the rising tide of artificial intelligence and business automation: developing an ethical framework,effective exposure amortizing for fair top-k recommendation,"wright sa,schultz ae","yang t,xu z,ai q","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
1060,the rising tide of artificial intelligence and business automation: developing an ethical framework,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","wright sa,schultz ae","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
1061,the rising tide of artificial intelligence and business automation: developing an ethical framework,flexible and adaptive fairness-aware learning in non-stationary data streams,"wright sa,schultz ae","zhang w,zhang m,zhang j,liu z,...","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","ai fairness,flexible fairness,online classification",1
1062,the rising tide of artificial intelligence and business automation: developing an ethical framework,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","wright sa,schultz ae","zhou n,zhang z,nair vn,singhal h,chen j","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","ai,ml,algorithm,bias,fairness",0
1063,the rising tide of artificial intelligence and business automation: developing an ethical framework,applying differential privacy mechanism in artificial intelligence,"wright sa,schultz ae","zhu t,yu ps","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
1064,achieving causal fairness through generative adversarial networks,transparency and explainability of ai systems: ethical guidelines in practice,"xu d,wu y,yuan s,zhang l,wu x","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","ai systems,ethical guidelines,explainability,quality requirements,transparency",1
1065,achieving causal fairness through generative adversarial networks,"artificial intelligence, intersectionality, and the future of public health","xu d,wu y,yuan s,zhang l,wu x","bauer gr,lizotte dj","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
1066,achieving causal fairness through generative adversarial networks,fairness & friends in the data science era,"xu d,wu y,yuan s,zhang l,wu x","catania b,guerrini g,accinelli c","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",1
1067,achieving causal fairness through generative adversarial networks,a sociotechnical view of algorithmic fairness,"xu d,wu y,yuan s,zhang l,wu x","dolata m,feuerriegel s,schwabe g","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
1068,achieving causal fairness through generative adversarial networks,ai-enabled radio resource allocation in 5g for urllc and embb users,"xu d,wu y,yuan s,zhang l,wu x","elsayed m,erol-kantarci m","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
1069,achieving causal fairness through generative adversarial networks,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","xu d,wu y,yuan s,zhang l,wu x","fletcher rr,nakeshimana a,...","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",1
1070,achieving causal fairness through generative adversarial networks,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"xu d,wu y,yuan s,zhang l,wu x","giovanola b,tiribelli s","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
1071,achieving causal fairness through generative adversarial networks,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"xu d,wu y,yuan s,zhang l,wu x","helberger n,araujo t,de vreese ch","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","algorithmic fairness,automated-decision making,public perceptions",0
1072,achieving causal fairness through generative adversarial networks,principal fairness for human and algorithmic decision-making,"xu d,wu y,yuan s,zhang l,wu x","imai k,jiang z","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
1073,achieving causal fairness through generative adversarial networks,"introduction to the special issue on ai fairness, trust, and ethics","xu d,wu y,yuan s,zhang l,wu x","jr lp,bansal g,melville n,...","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","ai bias,ai fairness,ai trust,artificial intelligence",0
1074,achieving causal fairness through generative adversarial networks,exploring bias and fairness in artificial intelligence and machine learning algorithms,"xu d,wu y,yuan s,zhang l,wu x","khakurel ub,abdelmoumin g,bajracharya a,rawat db","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
1075,achieving causal fairness through generative adversarial networks,ai & intellectual property: towards an articulated public domain,"xu d,wu y,yuan s,zhang l,wu x",kop m,"humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
1076,achieving causal fairness through generative adversarial networks,a survey on datasets for fairness-aware machine learning,"xu d,wu y,yuan s,zhang l,wu x","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
1077,achieving causal fairness through generative adversarial networks,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"xu d,wu y,yuan s,zhang l,wu x","li c,xing w","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","fair ai,human-centered computing,math learning,perceived fairness",0
1078,achieving causal fairness through generative adversarial networks,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"xu d,wu y,yuan s,zhang l,wu x","li j,chignell textperiodcenteredm","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
1079,achieving causal fairness through generative adversarial networks,trustworthy ai: from principles to practices,"xu d,wu y,yuan s,zhang l,wu x","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
1080,achieving causal fairness through generative adversarial networks,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","xu d,wu y,yuan s,zhang l,wu x","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","ai,fairness,machine learning,software development practices",1
1081,achieving causal fairness through generative adversarial networks,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","xu d,wu y,yuan s,zhang l,wu x","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
1082,achieving causal fairness through generative adversarial networks,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"xu d,wu y,yuan s,zhang l,wu x","pessach d,shmueli e","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",1
1083,achieving causal fairness through generative adversarial networks,artificial intelligence in human resources management: challenges and a path forward,"xu d,wu y,yuan s,zhang l,wu x","tambe p,cappelli p,yakubovich v","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
1084,achieving causal fairness through generative adversarial networks,ai ethics in industry: a research framework,"xu d,wu y,yuan s,zhang l,wu x","ville v,kai-kristian k,pekka a","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
1085,achieving causal fairness through generative adversarial networks,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"xu d,wu y,yuan s,zhang l,wu x","wachter s,mittelstadt b,russell c","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",1
1086,achieving causal fairness through generative adversarial networks,user association and resource allocation in full-duplex relay aided noma systems,"xu d,wu y,yuan s,zhang l,wu x","wang l,ai y,liu n,fei a","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
1087,achieving causal fairness through generative adversarial networks,artificial intelligence and the public sector—applications and challenges,"xu d,wu y,yuan s,zhang l,wu x","wirtz bw,weyerer jc,geyer c","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","ai applications,ai challenges,artificial intelligence,public sector",1
1088,achieving causal fairness through generative adversarial networks,algorithmic fairness in computational medicine,"xu d,wu y,yuan s,zhang l,wu x","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","algorithmic fairness,computational medicine",0
1089,achieving causal fairness through generative adversarial networks,effective exposure amortizing for fair top-k recommendation,"xu d,wu y,yuan s,zhang l,wu x","yang t,xu z,ai q","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
1090,achieving causal fairness through generative adversarial networks,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","xu d,wu y,yuan s,zhang l,wu x","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
1091,achieving causal fairness through generative adversarial networks,flexible and adaptive fairness-aware learning in non-stationary data streams,"xu d,wu y,yuan s,zhang l,wu x","zhang w,zhang m,zhang j,liu z,...","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","ai fairness,flexible fairness,online classification",0
1092,achieving causal fairness through generative adversarial networks,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","xu d,wu y,yuan s,zhang l,wu x","zhou n,zhang z,nair vn,singhal h,chen j","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","ai,ml,algorithm,bias,fairness",1
1093,achieving causal fairness through generative adversarial networks,applying differential privacy mechanism in artificial intelligence,"xu d,wu y,yuan s,zhang l,wu x","zhu t,yu ps","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
1094,algorithmic fairness in computational medicine,transparency and explainability of ai systems: ethical guidelines in practice,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","algorithmic fairness,computational medicine","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
1095,algorithmic fairness in computational medicine,"artificial intelligence, intersectionality, and the future of public health","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","bauer gr,lizotte dj","algorithmic fairness,computational medicine","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
1096,algorithmic fairness in computational medicine,fairness & friends in the data science era,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","catania b,guerrini g,accinelli c","algorithmic fairness,computational medicine","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
1097,algorithmic fairness in computational medicine,a sociotechnical view of algorithmic fairness,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","dolata m,feuerriegel s,schwabe g","algorithmic fairness,computational medicine","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
1098,algorithmic fairness in computational medicine,ai-enabled radio resource allocation in 5g for urllc and embb users,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","elsayed m,erol-kantarci m","algorithmic fairness,computational medicine","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
1099,algorithmic fairness in computational medicine,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","fletcher rr,nakeshimana a,...","algorithmic fairness,computational medicine","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
1100,algorithmic fairness in computational medicine,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","giovanola b,tiribelli s","algorithmic fairness,computational medicine","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",1
1101,algorithmic fairness in computational medicine,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","helberger n,araujo t,de vreese ch","algorithmic fairness,computational medicine","algorithmic fairness,automated-decision making,public perceptions",1
1102,algorithmic fairness in computational medicine,principal fairness for human and algorithmic decision-making,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","imai k,jiang z","algorithmic fairness,computational medicine","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
1103,algorithmic fairness in computational medicine,"introduction to the special issue on ai fairness, trust, and ethics","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","jr lp,bansal g,melville n,...","algorithmic fairness,computational medicine","ai bias,ai fairness,ai trust,artificial intelligence",1
1104,algorithmic fairness in computational medicine,exploring bias and fairness in artificial intelligence and machine learning algorithms,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","khakurel ub,abdelmoumin g,bajracharya a,rawat db","algorithmic fairness,computational medicine","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
1105,algorithmic fairness in computational medicine,ai & intellectual property: towards an articulated public domain,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f",kop m,"algorithmic fairness,computational medicine","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
1106,algorithmic fairness in computational medicine,a survey on datasets for fairness-aware machine learning,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","algorithmic fairness,computational medicine","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",1
1107,algorithmic fairness in computational medicine,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","li c,xing w","algorithmic fairness,computational medicine","fair ai,human-centered computing,math learning,perceived fairness",0
1108,algorithmic fairness in computational medicine,trustworthy ai: from principles to practices,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","algorithmic fairness,computational medicine","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
1109,algorithmic fairness in computational medicine,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","algorithmic fairness,computational medicine","ai,fairness,machine learning,software development practices",0
1110,algorithmic fairness in computational medicine,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","algorithmic fairness,computational medicine","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
1111,algorithmic fairness in computational medicine,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","pessach d,shmueli e","algorithmic fairness,computational medicine","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",1
1112,algorithmic fairness in computational medicine,artificial intelligence in human resources management: challenges and a path forward,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","tambe p,cappelli p,yakubovich v","algorithmic fairness,computational medicine","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
1113,algorithmic fairness in computational medicine,ai ethics in industry: a research framework,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","ville v,kai-kristian k,pekka a","algorithmic fairness,computational medicine","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
1114,algorithmic fairness in computational medicine,artificial intelligence and the public sector—applications and challenges,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","wirtz bw,weyerer jc,geyer c","algorithmic fairness,computational medicine","ai applications,ai challenges,artificial intelligence,public sector",0
1115,algorithmic fairness in computational medicine,effective exposure amortizing for fair top-k recommendation,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","yang t,xu z,ai q","algorithmic fairness,computational medicine","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
1116,algorithmic fairness in computational medicine,flexible and adaptive fairness-aware learning in non-stationary data streams,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","zhang w,zhang m,zhang j,liu z,...","algorithmic fairness,computational medicine","ai fairness,flexible fairness,online classification",0
1117,algorithmic fairness in computational medicine,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","zhou n,zhang z,nair vn,singhal h,chen j","algorithmic fairness,computational medicine","ai,ml,algorithm,bias,fairness",0
1118,algorithmic fairness in computational medicine,applying differential privacy mechanism in artificial intelligence,"xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","zhu t,yu ps","algorithmic fairness,computational medicine","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
1119,effective exposure amortizing for fair top-k recommendation,"artificial intelligence, intersectionality, and the future of public health","yang t,xu z,ai q","bauer gr,lizotte dj","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
1120,effective exposure amortizing for fair top-k recommendation,a sociotechnical view of algorithmic fairness,"yang t,xu z,ai q","dolata m,feuerriegel s,schwabe g","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
1121,effective exposure amortizing for fair top-k recommendation,ai-enabled radio resource allocation in 5g for urllc and embb users,"yang t,xu z,ai q","elsayed m,erol-kantarci m","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
1122,effective exposure amortizing for fair top-k recommendation,principal fairness for human and algorithmic decision-making,"yang t,xu z,ai q","imai k,jiang z","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
1123,effective exposure amortizing for fair top-k recommendation,"introduction to the special issue on ai fairness, trust, and ethics","yang t,xu z,ai q","jr lp,bansal g,melville n,...","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","ai bias,ai fairness,ai trust,artificial intelligence",0
1124,effective exposure amortizing for fair top-k recommendation,ai & intellectual property: towards an articulated public domain,"yang t,xu z,ai q",kop m,"ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
1125,effective exposure amortizing for fair top-k recommendation,a survey on datasets for fairness-aware machine learning,"yang t,xu z,ai q","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
1126,effective exposure amortizing for fair top-k recommendation,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"yang t,xu z,ai q","li c,xing w","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","fair ai,human-centered computing,math learning,perceived fairness",0
1127,effective exposure amortizing for fair top-k recommendation,trustworthy ai: from principles to practices,"yang t,xu z,ai q","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
1128,effective exposure amortizing for fair top-k recommendation,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","yang t,xu z,ai q","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
1129,effective exposure amortizing for fair top-k recommendation,artificial intelligence in human resources management: challenges and a path forward,"yang t,xu z,ai q","tambe p,cappelli p,yakubovich v","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
1130,effective exposure amortizing for fair top-k recommendation,artificial intelligence and the public sector—applications and challenges,"yang t,xu z,ai q","wirtz bw,weyerer jc,geyer c","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","ai applications,ai challenges,artificial intelligence,public sector",0
1131,effective exposure amortizing for fair top-k recommendation,flexible and adaptive fairness-aware learning in non-stationary data streams,"yang t,xu z,ai q","zhang w,zhang m,zhang j,liu z,...","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","ai fairness,flexible fairness,online classification",0
1132,effective exposure amortizing for fair top-k recommendation,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","yang t,xu z,ai q","zhou n,zhang z,nair vn,singhal h,chen j","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation","ai,ml,algorithm,bias,fairness",0
1133,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",transparency and explainability of ai systems: ethical guidelines in practice,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
1134,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","artificial intelligence, intersectionality, and the future of public health","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","bauer gr,lizotte dj","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
1135,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",fairness & friends in the data science era,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","catania b,guerrini g,accinelli c","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
1136,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",a sociotechnical view of algorithmic fairness,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","dolata m,feuerriegel s,schwabe g","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
1137,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",ai-enabled radio resource allocation in 5g for urllc and embb users,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","elsayed m,erol-kantarci m","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
1138,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","fletcher rr,nakeshimana a,...","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
1139,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","giovanola b,tiribelli s","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
1140,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","helberger n,araujo t,de vreese ch","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","algorithmic fairness,automated-decision making,public perceptions",0
1141,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",principal fairness for human and algorithmic decision-making,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","imai k,jiang z","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
1142,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","introduction to the special issue on ai fairness, trust, and ethics","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","jr lp,bansal g,melville n,...","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","ai bias,ai fairness,ai trust,artificial intelligence",0
1143,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",exploring bias and fairness in artificial intelligence and machine learning algorithms,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","khakurel ub,abdelmoumin g,bajracharya a,rawat db","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",0
1144,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",ai & intellectual property: towards an articulated public domain,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m",kop m,"computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
1145,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",a survey on datasets for fairness-aware machine learning,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
1146,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","li c,xing w","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","fair ai,human-centered computing,math learning,perceived fairness",0
1147,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","li j,chignell textperiodcenteredm","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
1148,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",trustworthy ai: from principles to practices,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
1149,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","ai,fairness,machine learning,software development practices",0
1150,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
1151,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","pessach d,shmueli e","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",0
1152,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",artificial intelligence in human resources management: challenges and a path forward,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","tambe p,cappelli p,yakubovich v","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
1153,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",ai ethics in industry: a research framework,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","ville v,kai-kristian k,pekka a","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
1154,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","wachter s,mittelstadt b,russell c","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
1155,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",user association and resource allocation in full-duplex relay aided noma systems,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","wang l,ai y,liu n,fei a","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
1156,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",artificial intelligence and the public sector—applications and challenges,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","wirtz bw,weyerer jc,geyer c","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","ai applications,ai challenges,artificial intelligence,public sector",0
1157,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",algorithmic fairness in computational medicine,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","algorithmic fairness,computational medicine",0
1158,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",effective exposure amortizing for fair top-k recommendation,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","yang t,xu z,ai q","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
1159,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",flexible and adaptive fairness-aware learning in non-stationary data streams,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","zhang w,zhang m,zhang j,liu z,...","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","ai fairness,flexible fairness,online classification",0
1160,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","bias, fairness and accountability with artificial intelligence and machine learning algorithms","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","zhou n,zhang z,nair vn,singhal h,chen j","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","ai,ml,algorithm,bias,fairness",0
1161,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology",applying differential privacy mechanism in artificial intelligence,"zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","zhu t,yu ps","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
1162,flexible and adaptive fairness-aware learning in non-stationary data streams,"artificial intelligence, intersectionality, and the future of public health","zhang w,zhang m,zhang j,liu z,...","bauer gr,lizotte dj","ai fairness,flexible fairness,online classification","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
1163,flexible and adaptive fairness-aware learning in non-stationary data streams,ai-enabled radio resource allocation in 5g for urllc and embb users,"zhang w,zhang m,zhang j,liu z,...","elsayed m,erol-kantarci m","ai fairness,flexible fairness,online classification","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
1164,flexible and adaptive fairness-aware learning in non-stationary data streams,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","zhang w,zhang m,zhang j,liu z,...","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","ai fairness,flexible fairness,online classification","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
1165,introduction to ai fairness,transparency and explainability of ai systems: ethical guidelines in practice,"zhang y,bellamy r,liao qv,singh m","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","algorithmic fairness,bias,decision support,discrimination-aware machine","ai systems,ethical guidelines,explainability,quality requirements,transparency",0
1166,introduction to ai fairness,"artificial intelligence, intersectionality, and the future of public health","zhang y,bellamy r,liao qv,singh m","bauer gr,lizotte dj","algorithmic fairness,bias,decision support,discrimination-aware machine","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
1167,introduction to ai fairness,fairness & friends in the data science era,"zhang y,bellamy r,liao qv,singh m","catania b,guerrini g,accinelli c","algorithmic fairness,bias,decision support,discrimination-aware machine","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",1
1168,introduction to ai fairness,a sociotechnical view of algorithmic fairness,"zhang y,bellamy r,liao qv,singh m","dolata m,feuerriegel s,schwabe g","algorithmic fairness,bias,decision support,discrimination-aware machine","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
1169,introduction to ai fairness,ai-enabled radio resource allocation in 5g for urllc and embb users,"zhang y,bellamy r,liao qv,singh m","elsayed m,erol-kantarci m","algorithmic fairness,bias,decision support,discrimination-aware machine","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
1170,introduction to ai fairness,"addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health","zhang y,bellamy r,liao qv,singh m","fletcher rr,nakeshimana a,...","algorithmic fairness,bias,decision support,discrimination-aware machine","appropriate use,artificial intelligence,bias,ethics,fairness,global health,machine learning,medicine",0
1171,introduction to ai fairness,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"zhang y,bellamy r,liao qv,singh m","giovanola b,tiribelli s","algorithmic fairness,bias,decision support,discrimination-aware machine","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
1172,introduction to ai fairness,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"zhang y,bellamy r,liao qv,singh m","helberger n,araujo t,de vreese ch","algorithmic fairness,bias,decision support,discrimination-aware machine","algorithmic fairness,automated-decision making,public perceptions",0
1173,introduction to ai fairness,principal fairness for human and algorithmic decision-making,"zhang y,bellamy r,liao qv,singh m","imai k,jiang z","algorithmic fairness,bias,decision support,discrimination-aware machine","algorithmic fairness,causal inference,potential outcomes,principal stratification",0
1174,introduction to ai fairness,"introduction to the special issue on ai fairness, trust, and ethics","zhang y,bellamy r,liao qv,singh m","jr lp,bansal g,melville n,...","algorithmic fairness,bias,decision support,discrimination-aware machine","ai bias,ai fairness,ai trust,artificial intelligence",1
1175,introduction to ai fairness,exploring bias and fairness in artificial intelligence and machine learning algorithms,"zhang y,bellamy r,liao qv,singh m","khakurel ub,abdelmoumin g,bajracharya a,rawat db","algorithmic fairness,bias,decision support,discrimination-aware machine","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",0
1176,introduction to ai fairness,ai & intellectual property: towards an articulated public domain,"zhang y,bellamy r,liao qv,singh m",kop m,"algorithmic fairness,bias,decision support,discrimination-aware machine","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",0
1177,introduction to ai fairness,a survey on datasets for fairness-aware machine learning,"zhang y,bellamy r,liao qv,singh m","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","algorithmic fairness,bias,decision support,discrimination-aware machine","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
1178,introduction to ai fairness,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"zhang y,bellamy r,liao qv,singh m","li c,xing w","algorithmic fairness,bias,decision support,discrimination-aware machine","fair ai,human-centered computing,math learning,perceived fairness",0
1179,introduction to ai fairness,fmea-ai: ai fairness impact assessment using failure mode and effects analysis,"zhang y,bellamy r,liao qv,singh m","li j,chignell textperiodcenteredm","algorithmic fairness,bias,decision support,discrimination-aware machine","artificial intelligence,ethics,human rights impact assessments,proportional satisfaction of claims,risk analysis",0
1180,introduction to ai fairness,trustworthy ai: from principles to practices,"zhang y,bellamy r,liao qv,singh m","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","algorithmic fairness,bias,decision support,discrimination-aware machine","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
1181,introduction to ai fairness,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","zhang y,bellamy r,liao qv,singh m","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","algorithmic fairness,bias,decision support,discrimination-aware machine","ai,fairness,machine learning,software development practices",0
1182,introduction to ai fairness,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","zhang y,bellamy r,liao qv,singh m","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","algorithmic fairness,bias,decision support,discrimination-aware machine","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
1183,introduction to ai fairness,improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings,"zhang y,bellamy r,liao qv,singh m","pessach d,shmueli e","algorithmic fairness,bias,decision support,discrimination-aware machine","algorithmic bias,algorithmic fairness,fairness-aware machine learning,selection bias,semi-supervised learning",0
1184,introduction to ai fairness,linear versus nonlinear allocation rules in risk sharing under financial fairness,"zhang y,bellamy r,liao qv,singh m",schumacher jm,"algorithmic fairness,bias,decision support,discrimination-aware machine","borch theorem,risk sharing,allocation rules,financial fairness,syndicates",0
1185,introduction to ai fairness,artificial intelligence in human resources management: challenges and a path forward,"zhang y,bellamy r,liao qv,singh m","tambe p,cappelli p,yakubovich v","algorithmic fairness,bias,decision support,discrimination-aware machine","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",0
1186,introduction to ai fairness,ai ethics in industry: a research framework,"zhang y,bellamy r,liao qv,singh m","ville v,kai-kristian k,pekka a","algorithmic fairness,bias,decision support,discrimination-aware machine","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
1187,introduction to ai fairness,why fairness cannot be automated: bridging the gap between eu non-discrimination law and ai,"zhang y,bellamy r,liao qv,singh m","wachter s,mittelstadt b,russell c","algorithmic fairness,bias,decision support,discrimination-aware machine","algorithm,artificial intelligence,bias,demographic parity,discrimination,european union,fairness,law,machine learning,non-discrimination",0
1188,introduction to ai fairness,user association and resource allocation in full-duplex relay aided noma systems,"zhang y,bellamy r,liao qv,singh m","wang l,ai y,liu n,fei a","algorithmic fairness,bias,decision support,discrimination-aware machine","full-duplex (fd) relaying,nonorthogonal multiple access (noma),resource allocation",0
1189,introduction to ai fairness,artificial intelligence and the public sector—applications and challenges,"zhang y,bellamy r,liao qv,singh m","wirtz bw,weyerer jc,geyer c","algorithmic fairness,bias,decision support,discrimination-aware machine","ai applications,ai challenges,artificial intelligence,public sector",0
1190,introduction to ai fairness,the rising tide of artificial intelligence and business automation: developing an ethical framework,"zhang y,bellamy r,liao qv,singh m","wright sa,schultz ae","algorithmic fairness,bias,decision support,discrimination-aware machine","artificial intelligence,automation,business ethics,social contracts theory,stakeholder theory,work displacement",0
1191,introduction to ai fairness,achieving causal fairness through generative adversarial networks,"zhang y,bellamy r,liao qv,singh m","xu d,wu y,yuan s,zhang l,wu x","algorithmic fairness,bias,decision support,discrimination-aware machine","humans and ai: ethical issues in ai,machine learning: adversarial machine learning,machine learning: learning generative models",0
1192,introduction to ai fairness,algorithmic fairness in computational medicine,"zhang y,bellamy r,liao qv,singh m","xu j,xiao y,wang wh,ning y,shenkman ea,bian j,wang f","algorithmic fairness,bias,decision support,discrimination-aware machine","algorithmic fairness,computational medicine",1
1193,introduction to ai fairness,effective exposure amortizing for fair top-k recommendation,"zhang y,bellamy r,liao qv,singh m","yang t,xu z,ai q","algorithmic fairness,bias,decision support,discrimination-aware machine","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
1194,introduction to ai fairness,"""zhores"" - petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in skolkovo institute of science and technology","zhang y,bellamy r,liao qv,singh m","zacharov i,arslanov r,gunin m,stefonishin d,bykov a,pavlov s,panarin o,maliutin a,rykovanov s,fedorov m","algorithmic fairness,bias,decision support,discrimination-aware machine","computer scalability,computing clusters,energy efficiency,high performance computing,high speed networks,parallel computation",0
1195,introduction to ai fairness,flexible and adaptive fairness-aware learning in non-stationary data streams,"zhang y,bellamy r,liao qv,singh m","zhang w,zhang m,zhang j,liu z,...","algorithmic fairness,bias,decision support,discrimination-aware machine","ai fairness,flexible fairness,online classification",0
1196,introduction to ai fairness,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","zhang y,bellamy r,liao qv,singh m","zhou n,zhang z,nair vn,singhal h,chen j","algorithmic fairness,bias,decision support,discrimination-aware machine","ai,ml,algorithm,bias,fairness",0
1197,introduction to ai fairness,applying differential privacy mechanism in artificial intelligence,"zhang y,bellamy r,liao qv,singh m","zhu t,yu ps","algorithmic fairness,bias,decision support,discrimination-aware machine","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning",0
1198,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","artificial intelligence, intersectionality, and the future of public health","zhou n,zhang z,nair vn,singhal h,chen j","bauer gr,lizotte dj","ai,ml,algorithm,bias,fairness","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",0
1199,"bias, fairness and accountability with artificial intelligence and machine learning algorithms",a sociotechnical view of algorithmic fairness,"zhou n,zhang z,nair vn,singhal h,chen j","dolata m,feuerriegel s,schwabe g","ai,ml,algorithm,bias,fairness","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",1
1200,"bias, fairness and accountability with artificial intelligence and machine learning algorithms",ai-enabled radio resource allocation in 5g for urllc and embb users,"zhou n,zhang z,nair vn,singhal h,chen j","elsayed m,erol-kantarci m","ai,ml,algorithm,bias,fairness","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
1201,"bias, fairness and accountability with artificial intelligence and machine learning algorithms",revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"zhou n,zhang z,nair vn,singhal h,chen j","li c,xing w","ai,ml,algorithm,bias,fairness","fair ai,human-centered computing,math learning,perceived fairness",1
1202,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","zhou n,zhang z,nair vn,singhal h,chen j","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","ai,ml,algorithm,bias,fairness","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
1203,"bias, fairness and accountability with artificial intelligence and machine learning algorithms",flexible and adaptive fairness-aware learning in non-stationary data streams,"zhou n,zhang z,nair vn,singhal h,chen j","zhang w,zhang m,zhang j,liu z,...","ai,ml,algorithm,bias,fairness","ai fairness,flexible fairness,online classification",1
1204,applying differential privacy mechanism in artificial intelligence,transparency and explainability of ai systems: ethical guidelines in practice,"zhu t,yu ps","balasubramaniam n,kauppinen m,hiekkanen k,kujala s","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","ai systems,ethical guidelines,explainability,quality requirements,transparency",1
1205,applying differential privacy mechanism in artificial intelligence,"artificial intelligence, intersectionality, and the future of public health","zhu t,yu ps","bauer gr,lizotte dj","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","ajph,apha,american,association,health,journal,public,ethics,infrastructure,policy,practice",1
1206,applying differential privacy mechanism in artificial intelligence,fairness & friends in the data science era,"zhu t,yu ps","catania b,guerrini g,accinelli c","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","data-informed automated decision system,diversity,fairness,nondiscrimination,processing pipeline",0
1207,applying differential privacy mechanism in artificial intelligence,a sociotechnical view of algorithmic fairness,"zhu t,yu ps","dolata m,feuerriegel s,schwabe g","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","algorithmic fairness,literature review,problematization,research agenda,sociotechnical perspective,state of the art",0
1208,applying differential privacy mechanism in artificial intelligence,ai-enabled radio resource allocation in 5g for urllc and embb users,"zhu t,yu ps","elsayed m,erol-kantarci m","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","5g mobile communication,delays,quality of service,reliability,resource management,throughput",0
1209,applying differential privacy mechanism in artificial intelligence,beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,"zhu t,yu ps","giovanola b,tiribelli s","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","bias,discrimination,ethics of algorithms,fairness,healthcare machine-learning algorithms,respect",0
1210,applying differential privacy mechanism in artificial intelligence,who is the fairest of them all? public attitudes and expectations regarding automated decision-making,"zhu t,yu ps","helberger n,araujo t,de vreese ch","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","algorithmic fairness,automated-decision making,public perceptions",0
1211,applying differential privacy mechanism in artificial intelligence,principal fairness for human and algorithmic decision-making,"zhu t,yu ps","imai k,jiang z","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","algorithmic fairness,causal inference,potential outcomes,principal stratification",1
1212,applying differential privacy mechanism in artificial intelligence,"introduction to the special issue on ai fairness, trust, and ethics","zhu t,yu ps","jr lp,bansal g,melville n,...","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","ai bias,ai fairness,ai trust,artificial intelligence",0
1213,applying differential privacy mechanism in artificial intelligence,exploring bias and fairness in artificial intelligence and machine learning algorithms,"zhu t,yu ps","khakurel ub,abdelmoumin g,bajracharya a,rawat db","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","artificial intelligence,detection and tracking algorithms,machine learning,performance modeling,systems modeling",1
1214,applying differential privacy mechanism in artificial intelligence,ai & intellectual property: towards an articulated public domain,"zhu t,yu ps",kop m,"artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","founder of musicajuridica and strategic in- tellectual property lawyer at alrecht in amsterdam,james grimmelmann (cornell law school),joao pedro quintais (ivir) and suzan slijpen (slijpen legal),for their valuable contributions,insight and comments",1
1215,applying differential privacy mechanism in artificial intelligence,a survey on datasets for fairness-aware machine learning,"zhu t,yu ps","le quy t,roy a,iosifidis v,zhang w,ntoutsi e","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","aware machine learning,benchmark datasets,bias,datasets for fairness,discrimination,fairness",0
1216,applying differential privacy mechanism in artificial intelligence,revealing factors influencing students' perceived fairness: a case with a predictive system for math learning,"zhu t,yu ps","li c,xing w","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","fair ai,human-centered computing,math learning,perceived fairness",0
1217,applying differential privacy mechanism in artificial intelligence,trustworthy ai: from principles to practices,"zhu t,yu ps","libo,qipeng,liubo,dishuai,liujingen,peijiquan,yijinfeng,zhoubowen","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","trustworthy ai,accountability,explainability,fairness,generalization,privacy protection,reproducibility,robustness,transparency",0
1218,applying differential privacy mechanism in artificial intelligence,"assessing the fairness of ai systems: ai practitioners' processes, challenges, and needs for support","zhu t,yu ps","madaio m,egede l,subramonyam h,wortman vaughan j,wallach h","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","ai,fairness,machine learning,software development practices",0
1219,applying differential privacy mechanism in artificial intelligence,"ai4people - on good ai governance: 14 priority actions, a s.m.a.r.t. model of governance, and a regulatory toolbox","zhu t,yu ps","pagallo u,aurucci p,casanovas p,chatila r,chazerand p,dignum v,luetge c,madelin r,schafer b,valcke p","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","ai4people - on good ai governance: 14 priority actions,artificial intelligence,burkhard schafer,christoph luetge,governance,paola aurucci,patrice chazerand,peggy valcke,pompeu casanovas,raja chatila,regulation,robert madelin,ssrn,ugo pagallo,virginia dignum,a s.m.a.r.t. model of governance,and a regulatory toolbox",0
1220,applying differential privacy mechanism in artificial intelligence,artificial intelligence in human resources management: challenges and a path forward,"zhu t,yu ps","tambe p,cappelli p,yakubovich v","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","data analysis,decision-making tools,hiring and recruitment,human capital,human resource ethics,information systems",1
1221,applying differential privacy mechanism in artificial intelligence,ai ethics in industry: a research framework,"zhu t,yu ps","ville v,kai-kristian k,pekka a","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","ai development,ai ethics,accountability,artificial intelligence,research framework,responsibility,transparency",0
1222,applying differential privacy mechanism in artificial intelligence,artificial intelligence and the public sector—applications and challenges,"zhu t,yu ps","wirtz bw,weyerer jc,geyer c","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","ai applications,ai challenges,artificial intelligence,public sector",1
1223,applying differential privacy mechanism in artificial intelligence,effective exposure amortizing for fair top-k recommendation,"zhu t,yu ps","yang t,xu z,ai q","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","ccs concepts • information systems → recommender systems; learning to rank keywords fair ranking,exposure,position bias,top-í µí±˜ recommendation",0
1224,applying differential privacy mechanism in artificial intelligence,flexible and adaptive fairness-aware learning in non-stationary data streams,"zhu t,yu ps","zhang w,zhang m,zhang j,liu z,...","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","ai fairness,flexible fairness,online classification",0
1225,applying differential privacy mechanism in artificial intelligence,"bias, fairness and accountability with artificial intelligence and machine learning algorithms","zhu t,yu ps","zhou n,zhang z,nair vn,singhal h,chen j","artificial intelligence,differential privacy,federated learning,multi-agent system,reinforcement learning","ai,ml,algorithm,bias,fairness",1